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Regional unevenness and synergy of carbon emission reduction in China's green low-carbon circular economy

August 2023
Journal of Cleaner Production 420(8):138436

August 2023
420(8):138436

DOI:10.1016/j.jclepro.2023.138436

Authors:

Kaisheng Di

Tianjin University

Qiumei Shi

Aier Eye Hospital Group

Show all 8 authorsHide

This study draws upon general systems theory and Bronfenbrenner's theory of ecosystem studies. Utilizing panel data from 2000 to 2019 covering 30 provinces in China, this study applies the entropy value method to measure the index of high-quality development in China's Green Low-Carbon Circular Economy. Subsequently, the study employs the generalized matrix moment estimation to analyze the regional unevenness and synergistic pathways of carbon emission

Analysis framework.

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Average level of circular economy development in 30 Chinese provinces from 2000 to 2019.

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Journal of Cleaner Production xxx (xxxx) 138436

Contents lists available at ScienceDirect

Journal of Cleaner Production

journal homepage: www.elsevier.com/locate/jclepro

Regional unevenness and synergy of carbon emission reduction in China's

green low-carbon circular economy

Kaisheng Di a,bc,*, Weidong Chen a, Xingnian Zhang b, Qiumei Shi e, Quanling Cai a,b, Dongli Li a,d,

Caiping Liu a,b, Zhensheng Di b

aCollege of Management and Economics, Tianjin University, Tianjin, 300072, China

bCollege of Politics and Public Administration, Qinghai Minzu University, Xining, 810000, China

cDepartment of Party Committee, Party School of the Qinghai Provincial Committee of CPC, Xining, 810000, China

dCollege of Chunming, Hainan University, Haikou, 570228, China

eHealth Education Services Department, Xining Aier Eye Hospital, Xining, 810000, China

ARTICLE INFO

Handling Editor: Giovanni Baiocchi

Keywords:

China's green low-carbon circular economy

Carbon emission reduction

Regional unevenness

Synergy effects

ABSTRACT

This study draws upon general systems theory and Bronfenbrenner's theory of ecosystem studies. Utilizing panel

data from 2000 to 2019 covering 30 provinces in China, this study applies the entropy value method to measure

the index of high-quality development in China's Green Low-Carbon Circular Economy. Subsequently, the study

employs the generalized matrix moment estimation to analyze the regional unevenness and synergistic pathways

of carbon emission reduction in the low-carbon circular economy. Findings indicate that carbon emission reduc-

tion within China's green, low-carbon circular economy exhibited inter-regional variations from 2000 to 2019,

accompanied by a significant non-equilibrium spatial distribution and a gradual evolutionary pattern. Except for

the northeast region, the deviation of the high-quality development index in China's Green Low-Carbon Circular

Economy from the national and regional averages converged across the eastern, central, western, and northeast-

ern regions, assuming control of other factors. Therefore, China takes action to stimulate the development mo-

mentum of each region, expediting the adoption of green production methods. Furthermore, optimizing the pol-

icy system should be prioritized to address the regional development gap. Additionally, implementing a compre-

hensive and intensive strategy to enhance resource recycling efficiency is crucial. These measures aim to provide

valuable insights for decision-makers in formulating strategies for the transformation toward China's Green Low-

Carbon Circular Economy.

Abbreviations table

Abbreviations Full forms

IRC Industrial Resource Cycle

CE Carbon Emissions

CI Carbon Intensity

CEc Circular Economy

CSY China Statistical Yearbook

ISe Industrial Structure

HId High Industrialization

DIs Dominant Industry Structure

ESt Energy Mix

URd Urban Development

IRn Industrial Rationalization

1. Introduction

Developing a green, low-carbon circular economy has become a piv-

otal pathway toward global sustainable development, garnering signifi-

cant scholarly interest(Goyal et al., 2021). Within the domain of carbon

emission reduction, scholars are dedicated to investigating strategies

for mitigating carbon emissions in response to challenges such as cli-

*Corresponding author. College of Management and Economics, Tianjin University, Tianjin, 300072, China

E-mail addresses: dikaisheng@tju.edu.cn (K. Di), chenweidong@tju.edu.cn (W. Chen), qhjameszhang@163.com (X. Zhang), shiqiumei2022@163.com

(Q. Shi), 1022209089@tju.edu.cn (Q. Cai), lidongli@tju.edu.cn (D. Li), liucaiping@tju.edu.cn (C. Liu), dizhensheng2023@163.com (Z. Di).

https://doi.org/10.1016/j.jclepro.2023.138436

Received 10 May 2023; Received in revised form 6 August 2023; Accepted 11 August 2023

0959-6526/© 20XX

Note: Low-resolution images were used to create this PDF. The original images will be used in the final composition.

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K. Di et al. Journal of Cleaner Production xxx (xxxx) 138436

mate change and environmental pollution(Palmer et al., 1997). Re-

searchers focus on carbon emissions within diverse industries and sec-

tors by exploring the efficacy of emission reduction technologies and

policies. Notably, extensive research has been conducted on measures

such as clean energy development, enhancing energy efficiency, and

fostering low-carbon transportation(Morseletto, 2020a). Furthermore,

the circular economy, recognized as a vital element of green and low-

carbon progress, has garnered significant attention(Stahel, 2016). The

circular economy underscores the imperative of efficient resource uti-

lization, recycling, and product design optimization to alleviate the en-

vironmental impact and curtail resource consumption(Murray et al.,

2017). Pertinent studies concentrate on technologies and mechanisms

for resource recycling, encompassing waste treatment and recovery,

circular agriculture, and circular manufacturing(Suchek et al., 2021).

Nevertheless, significant spatial disparities exist in developing a

green,low-carbon circular economy across regions, attributable to geo-

graphical location, economic development level, resource distribution,

and policy support(Hao et al., 2017). Geographical factors significantly

shape various regions' energy structures and industrial layouts, subse-

quently affecting the magnitude of carbon emissions(Nabernegg et al.,

2019). Disparities in economic development levels and industrial struc-

tures result in notable variations in carbon emissions across regions,

with developed regions generally exhibiting lower levels of carbon

emissions while less economically developed regions encounter more

significant challenges in reducing their carbon footprints(Suárez-Eiroa

et al., 2021). Moreover, the spatial distribution is significantly influ-

enced by resource allocation, particularly the availability of renewable

energy resources like wind, solar, and hydro energy, which afford op-

portunities for low-carbon development(Minunno et al., 2020). Con-

versely, regions with limited energy resources often rely on conven-

tional sources, elevating carbon emissions(Gigli et al., 2019). As a re-

sult, divergent resource endowments contribute to disparities in carbon

reduction potential and spatial distribution(Corvellec et al., 2022).

Being one of the world's major carbon-emitting nations, China faces

the immense challenge of reducing carbon emissions(Korhonen et al.,

2018). The Chinese government places great importance on reducing

carbon emissions and has implemented comprehensive policies and

measures to promote green and low-carbon development(MacArthur,

2013). Nonetheless, substantial spatial imbalances exist in China's ef-

forts to reduce carbon emissions, resulting from variations in geography

and economic structure(Geng et al., 2019). Effectively coordinating

carbon emission reduction and economic development has become a

pressing issue demanding immediate attention(Yong, 2007). Scholars

have endeavored to uncover variations in carbon emissions across dif-

ferent regions and investigate the spatial distribution patterns concern-

ing economic development and environmental protection(Rajput and

Singh, 2019). However, there needs to be more systematic research ad-

dressing the spatial imbalances and synergistic effects of carbon emis-

sion reduction(Moraga et al., 2019a). Notably, a significant research

gap exists concerning the examination of the present state and potential

of carbon emission reduction in diverse regions within the framework

of a green, low-carbon circular economy system, as well as the analysis

of disparities and complementarities in carbon emission reduction

across these regions(Velenturf and Purnell, 2021).

This paper employs empirical methods and spatial analysis rooted in

general systems theory and Bronfenbrenner's ecosystem theory to in-

vestigate the spatial disparities and synergistic impacts of carbon emis-

sion reduction(Van Eemeren and Grootendorst, 2004). In contrast to

traditional carbon emission reduction studies that often overlook the

role of geographic space, this study will utilize spatial analysis tools, in-

cluding geographic information systems (GIS) and spatial statistical

methods, to comprehensively and accurately examine the spatial attrib-

utes and variations of carbon emission reduction in different regions by

accounting for factors such as geographical location, spatial associa-

tion, and interregional interactions(Geissdoerfer et al., 2017). Emphasis

is on investigating the synergistic effects among different regions. By

examining the disparities and interdependencies in carbon emission re-

duction between regions, this study aims to explore the potential for

synergizing carbon emission reduction policies across regions. The

study will analyze the complementarities and synergistic mechanisms

of carbon emission reduction among different regions to facilitate re-

source sharing, technology transfer, and collaborative development, ul-

timately achieving synergistic effects in carbon emission reduction. A

systematic analytical framework will develop to comprehensively as-

sess the spatial disparities and synergistic impacts of carbon emission

reduction. The framework will consider multiple factors, including re-

gional economic development levels, industrial structures, energy

structures, and resource endowments. It will assess the potential for

carbon emission reduction and the synergistic mechanisms across dif-

ferent regions using quantitative and qualitative methods. By conduct-

ing a comprehensive investigation into the spatial disparities and syner-

gistic impacts of carbon emission reduction in China's green, low-

carbon circular economy, this study can offer a scientific foundation for

formulating carbon emission reduction policies by the government. By

comprehending the variations in carbon emissions among different re-

gions, targeted policies and measures can devise to optimize the advan-

tages of carbon emission reduction. This study can identify more effec-

tive approaches and strategies to mitigate carbon emissions and en-

hance resource efficiency by examining the spatial disparities and syn-

ergistic impacts of carbon emission reduction. This endeavor will facili-

tate the advancement of China's economy towards sustainability, miti-

gate environmental impact, and foster a virtuous cycle of economic de-

velopment and ecological preservation. This study can furnish a scien-

tific foundation for governmental decision-making, foster inter-regional

cooperation and knowledge exchange, and facilitate sustainable devel-

opment. It will enable China to tackle climate change and attain sus-

tainable economic development proactively.

This paper comprises six main sections. The first section provides an

introduction to the topic. The second section focuses on conducting a

literature review in the field. The third section elaborates on the theo-

retical model and presents the research hypotheses. In Part IV, the re-

search methodology is presented, and data collection is conducted. The

fifth section analyzes the results obtained from the empirical study. The

potential innovative aspects of this research are outlined below. The

analysis framework utilized in this study is illustrated in Fig. 1.

2. Literature evaluation

2.1. Theoretical background

The concept of the circular economy has garnered increasing atten-

tion in recent years due to its potential as a sustainable alternative to

the traditional linear economic model(Ormazabal et al., 2018a). Unlike

the linear economy, which follows a “take-make-dispose”approach and

depletes natural resources while generating substantial waste, the cir-

cular economy aims to establish a closed-loop system where resources

are continuously reused, recycled, and repurposed, thereby promoting

a regenerative and sustainable economy(Camacho-Otero et al., 2018).

The principles of the circular economy revolve around the three pillars

of “reduce, reuse, and recycle.”The first step towards achieving a circu-

lar economy is reducing waste and resource consumption(Bonviu,

2014). This can be accomplished by implementing more efficient manu-

facturing processes, utilizing sustainable materials, and fostering sus-

tainable consumption patterns(Stahel, 2019). The second stage entails

encouraging the reuse of products and resources to prolong their lifes-

pan and minimize the need for new production(Barreiro-Gen and

Lozano, 2020). It may involve refurbishing, repurposing, and repairing

products and adopting business models prioritizing access to goods and

services over ownership(Keijer et al., 2019). Lastly, the circular econ-

omy greatly emphasizes recycling and material recovery to keep re-

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K. Di et al. Journal of Cleaner Production xxx (xxxx) 138436

Fig. 1. Analysis framework.

sources circulating within the economy for as long as possible(Yuan et

al., 2006). It entails effective recycling processes and strategies for ex-

tracting valuable materials from waste streams(Zink and Geyer, 2017).

The circular economy offers significant advantages, particularly its

positive environmental and climate impact(Winans et al., 2017). A cir-

cular economy can effectively mitigate the environmental footprint of

production and consumption by reducing waste and resource consump-

tion(Geisendorf and Pietrulla, 2018). Notably, it reduces greenhouse

gas emissions by minimizing the demand for new materials and energy-

intensive manufacturing processes(Kirchherr et al., 2018). For instance,

recycling aluminum cans instead of manufacturing new ones from raw

materials can result in energy savings of up to 95%. Moreover, the cir-

cular economy has great potential for economic growth and job cre-

ation(Elia et al., 2017). Fostering innovative business models prioritiz-

ing resource reuse and recycling creates new markets for second-hand

materials and products(Heshmati, 2017). It, in turn, creates opportuni-

ties for small businesses and entrepreneurs to develop novel goods and

services that contribute to a more sustainable economy(Geng et al.,

2013). Furthermore, the circular economy generates employment

prospects in sectors such as recycling, refurbishment, and repair, posi-

tively impacting workers in industries that may otherwise face chal-

lenges due to automation or globalization of job opportunities

(Wijkman and Skånberg, 2015).

Researchers are increasingly examining the synergistic relationship

between regional imbalances and carbon emission reduction in China's

green, low-carbon circular economy(N. Wang et al., 2019). Several fac-

tors have influenced regional imbalances, including geographical loca-

tion, natural resources, industrial structure, and policy interventions

(Yrjälä et al., 2022). Studies have demonstrated that these imbalances

can significantly impact carbon emissions, with regions characterized

by heavy industry or high energy consumption exhibiting higher emis-

sions. In comparison, those dominated by the service sector or renew-

able energy sources tend to have lower emissions(Wich et al., 2020).

Understanding regional imbalances is crucial for identifying the driving

forces behind carbon emissions and developing targeted mitigation

strategies(Vela et al., 2022). Concurrently, the circular economy con-

cept emphasizes the sustainable and efficient utilization of resources

through reduction, reuse, and recycling(Gallego-Schmid et al., 2020).

By decoupling economic growth from resource consumption and envi-

ronmental degradation, the circular economy promotes resource effi-

ciency, embraces renewable energy sources, and reduces waste, con-

tributing to carbon emission reduction and environmental sustainabil-

ity(Fang et al., 2017). However, it is essential to acknowledge that the

effectiveness of circular economy practices may vary across regions due

to disparities in infrastructure, policy support, and economic conditions

(Sarkar et al., 2022).

Research in coordination arises from the combined effect of multiple

factors, resulting in an outcome exceeding their contributions

(Budzianowski, 2017). In the context of carbon reduction, coordination

can achieving by integrating diverse strategies and measures across sec-

tors and regions(Khayyam et al., 2021). For instance, synergistic ap-

proaches may involve combining the development of renewable energy

sources with energy-efficient industrial practices or coordinating re-

gional initiatives to reduce carbon emissions(Herrador et al., 2022).

Understanding the potential for coordination in carbon reduction is cru-

cial for formulating effective policies and strategies that maximize the

environmental benefits of the circular economy(AliAkbari et al., 2021).

It can accomplish by exploring the theoretical foundations of regional

imbalances, the circular economy, and synergistic carbon while provid-

ing insights into the possibilities of regional collaboration and coordi-

nated efforts to mitigate carbon emissions and promote sustainable de-

velopment(M. A.-A. Khan et al., 2023).

2.2. Empirical studies

In regional carbon emission profile studies, numerous empirical in-

vestigations have examined China's regional carbon emission profile

(Balaji et al., 2020). Scholars have analyzed the spatial distribution of

carbon emissions among different provinces and identified the primary

contributors to regional carbon emissions(Tcvetkov et al., 2019). Their

findings reveal substantial disparities in carbon emissions across re-

gions, with industrialized provinces in the eastern coastal area exhibit-

ing higher emissions compared to less developed regions in western and

central China(Pires da Mata Costa et al., 2021). Some scholars have

conducted comprehensive assessments of carbon emissions across Chi-

na's provinces and emphasized the necessity of developing region-

specific strategies for carbon reduction based on each region's unique

characteristics(Yunan et al., 2021).

In the study of circular economy practices and carbon emission re-

duction, empirical research has explored the relationship between cir-

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K. Di et al. Journal of Cleaner Production xxx (xxxx) 138436

cular economy practices and carbon emission reduction(Kurniawan et

al., 2023). Scholars have investigated the impact of circular economy

practices on carbon emissions in China's manufacturing sector(Poveda

et al., 2019). The findings suggest that circular economy practices, such

as material recycling and reuse, significantly reduce carbon emissions

(Tauseef Hassan et al., 2023). Likewise, experts have examined the role

of circular economy policies in promoting carbon reduction in different

regions of China(Vujanović et al., 2022). Their findings indicate that

the implementation of circular economy policies, accompanied by sup-

portive measures and incentives, can effectively contribute to carbon

emission reduction at the regional level(Van Doren et al., 2018).

Additionally, several studies have explored the potential coordina-

tion in carbon emission reduction by integrating various strategies and

measures(Lehmann et al., 2022). Scholars have examined the synergis-

tic effects of renewable energy development and efficiency improve-

ments in reducing carbon emissions across Chinese provinces(Rehman

Khan et al., 2018). Their findings demonstrate that combining renew-

able energy deployment with energy efficiency measures leads to more

significant carbon emission reductions than isolated approaches

(Erythropel et al., 2018). Some researchers have also investigated the

potential for regional cooperation in achieving carbon emission reduc-

tions by establishing carbon trading alliances between provinces

(Christensen, 2021). The results suggest that inter-regional collabora-

tion and resource sharing can enhance the effectiveness of carbon re-

duction policies(Van Oorschot et al., 2022).

These empirical studies provide valuable insights into the regional

unevenness and coordination of carbon emission reduction in China's

green, low-carbon circular economy(Lehmann et al., 2022). They un-

derscore the significance of considering regional differences, imple-

menting circular economy practices, and exploring synergistic ap-

proaches to achieve effective and sustainable carbon emission reduc-

tions(Rehman Khan et al., 2018). On these empirical findings, this

study aims to examine further the dynamics and interactions within the

regional context of China's green, low-carbon circular economy and of-

fer insights for policy formulation and decision-making(Erythropel et

al., 2018).

2.3. Gap in the literature

While the existing empirical studies offer valuable insights into the

regional imbalances and coordination of carbon reduction in China's

green, low-carbon circular economy, there still needs to be more litera-

ture that requires further investigation(Christensen, 2021). These gaps

signify areas where additional research is necessary to advance our un-

derstanding of the subject and inform policy and decision-making(Van

Oorschot et al., 2022). Further in-depth analysis is needed to examine

the factors contributing to regional imbalances(Dewick et al., 2022).

Understanding the underlying drivers of regional carbon emissions,

such as industrial structure, energy consumption patterns, and techno-

logical capacity, can assist in developing targeted carbon reduction

strategies for different regions(Bombana and Ariza, 2019). Most empiri-

cal studies have focused on carbon emissions as the primary environ-

mental indicator(Kim and Jang, 2022). However, the circular economy

encompasses broader environmental objectives beyond carbon emis-

sion reduction, including waste management, resource efficiency, and

pollution prevention(Galatti and Baruque-Ramos, 2022). Future re-

search should integrate multiple environmental indicators to capture

the overall impact of circular economy practices on environmental sus-

tainability(Peng et al., 2020). Many empirical studies have provided

cross-sectional snapshots of regional carbon emissions and circular

economy practices(Elia et al., 2020). Longitudinal studies are necessary

to examine changes over time and evaluate the long-term effects of cir-

cular economy policies and practices in reducing carbon emissions

(Herrador et al., 2022). Such studies can provide insights into circular

economy interventions' sustainability and long-lasting impacts(J.

Zhang and Zhang, 2020). While some studies have explored the role of

circular economy policies in carbon reduction, there needs to be more

understanding regarding the actual implementation of these policies at

the regional level(Li et al., 2019). Investigating the challenges and op-

portunities in policy implementation and the factors influencing policy

effectiveness can facilitate evidence-based policy development and im-

plementation strategies(Van Ewijk et al., 2020).

3. Research hypotheses and theoretical mechanisms

3.1. Direct impacts of circular economy on carbon emissions

The circular economy promotes resource efficiency and material re-

cycling as a sustainable approach to resource management (Pires da

Mata Costa et al., 2021). The circular economy can significantly reduce

carbon by fostering resource efficiency, minimizing waste production,

and supporting renewable energy sources(Hao et al., 2017). The circu-

lar economy emphasizes resource efficiency, maximizing resource use,

and minimizing waste and emissions(Yunan et al., 2021). Resource effi-

ciency and the circular economy can significantly reduce greenhouse

gas emissions associated with raw materials extraction, refining, and

transportation(N. Wang et al., 2019). For instance, using recycled mate-

rials in production reduces the demand for virgin materials and the as-

sociated emissions from their extraction and transportation. The circu-

lar economy promotes resource efficiency by encouraging the reuse of

materials and recycling to minimize waste (Wijkman and Skånberg,

2015). By extending the lifespan of goods and materials, the circular

economy can reduce emissions associated with the production and dis-

posal of waste(Kurniawan et al., 2023). Reusing products and recycling

materials can reduce landfill disposal or incineration emissions. The cir-

cular economy can contribute to reducing emissions associated with en-

ergy generation(MacArthur, 2013). The circular economy can help re-

duce greenhouse gas emissions associated with energy production by

promoting sustainable energy sources such as wind and solar(Geng et

al., 2019). The circular economy can promote the use of energy-

efficient tools and methods, thereby reducing emissions associated with

energy generation(MacArthur, 2013).

Several examples can illustrate the impact of the circular economy

on carbon emissions reduction efforts (MacArthur, 2013). The Ellen

MacArthur Foundation estimates that the circular economy has the po-

tential to reduce greenhouse gas pollution by 45% by 2030, assuming

emissions associated with waste generation, disposal, resource extrac-

tion, transportation, and production are reduced(Sarkar et al., 2022).

An example of this is the city of Amsterdam, which has implemented a

circular economy strategy to reduce its carbon footprint. The strategy

aims to establish a circular economy by 2050 and achieve a 50% reduc-

tion in greenhouse gas emissions by 2025(Khayyam et al., 2021). The

plan includes initiatives such as employing renewable energy sources,

promoting resource efficiency, reducing waste, and developing circular

business models to achieve these objectives(Balaji et al., 2020). The cir-

cular economy can assist specific sectors in lowering their emissions,

such as the textile sector, which is particularly notorious for its green-

house gas emissions and waste generation (Moraga et al., 2019a). The

circular economy promotes sustainable practices in the textile industry

by using recycled materials, waste reduction, and adopting sustainable

production techniques (Gigli et al., 2019). The building sector is an-

other area where the circular economy can significantly reduce carbon

emissions. The construction industry accounts for a substantial share of

greenhouse gas emissions, mainly attributed to building materials and

structures' production, transportation, and energy use (Stahel, 2016).

The circular economy can drive sustainable practices in the building

sector through increased utilization of recycled materials, waste reduc-

tion, and promotion of renewable energy sources (MacArthur, 2013).

The circular economy promotes resource efficiency, waste reduction,

and the utilization of renewable energy sources, leading to significant

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K. Di et al. Journal of Cleaner Production xxx (xxxx) 138436

reductions in greenhouse gas emissions. Numerous examples across dif-

ferent industries and sectors illustrate the impact of the circular econ-

omy on carbon reduction (Khayyam et al., 2021). The circular economy

requires a collaborative effort from all stakeholders, including govern-

ments, businesses, and consumers, to promote sustainable practices and

build a more sustainable future, thus unlocking its full potential

(Fackler et al., 2021).

Hypothesis 1. (H1). The growth of the circular economy can poten-

tially drastically lower carbon emissions.

Hypothesis 2. (H2). The development of the circular economy influ-

ences the number of carbon emissions per person.

3.2. Mechanisms relating to how the circular economy affects carbon

emissions

The circular economy advocates for resource efficiency and material

recycling as a sustainable approach to resource management

(Ormazabal et al., 2018a). The circular economy encompasses multiple

mechanisms contributing to its impact on carbon emissions in the con-

text of carbon reduction (Geisendorf and Pietrulla, 2018). The circular

economy aids in reducing carbon emissions by minimizing the use of

virgin materials(Ormazabal et al., 2018a). The circular economy miti-

gates the demand for new materials and the associated pollution by pro-

moting using recycled materials (Bonviu, 2014). For example, produc-

ing aluminum from recycled materials requires only 5% of the energy

needed for aluminum production from virgin materials, significantly re-

ducing greenhouse gas emissions. Similarly, using recycled plastics re-

duces emissions associated with virgin plastic manufacturing, oil ex-

traction, transportation, and waste plastic disposal(Kurniawan et al.,

2023).

Waste reduction is another approach through which the circular

economy influences carbon emissions. The circular economy promotes

material reuse and recycling, which reduces waste and the associated

emissions ((Wei et al., 2015). For example, recycling paper reduces

emissions from the disposal or incineration of paper waste. Likewise,

recycling goods such as electronics or clothing reduces emissions asso-

ciated with producing new goods and their disposal (Magazzino and

Falcone, 2022). Additionally, the circular economy can reduce carbon

pollution by promoting the adoption of renewable energy sources. The

circular economy can promote their use by creating circular business

models prioritizing sustainable energy sources like wind and solar

(AliAkbari et al., 2021). For example, circular business models that pro-

mote using recycled materials can also prioritize incorporating sustain-

able energy sources in their manufacturing processes (Kurniawan et al.,

2023).

Moreover, by promoting localized production and consumption, the

circular economy can reduce emissions associated with transportation.

Localized production and consumption can reduce emissions associated

with the transportation of commodities by minimizing travel distances

(Yong, 2007). For example, food grown locally has a lower environ-

mental impact in terms of pollutants than food transported over long

distances. Lastly, the circular economy can reduce carbon emissions by

promoting sustainable practices across multiple sectors. The circular

economy can support sustainable practices by minimizing waste and

urging the use of renewable energy sources(Corvellec et al., 2022). For

example, circular business models in the textile industry can promote

sustainable practices and reduce emissions associated with manufactur-

ing and disposing of textile goods by utilizing recycled materials and

minimizing waste(Pires da Mata Costa et al., 2021).

3.2.1. Carbon emissions, urban development, and the circular economy

The circular economy is an effective corporate strategy that priori-

tizes reusing, repairing, and recycling materials and goods to reduce

waste and resource consumption(Magazzino and Falcone, 2022). This

approach can significantly reduce carbon emissions in the context of ur-

ban development, considering that cities contribute a significant share

of global carbon emissions (Fang et al., 2017). This essay will explore

the interconnection between the circular economy, urban growth, and

carbon emissions. The circular economy can play a significant role in

reducing carbon emissions in the context of urban growth (Magazzino

and Falcone, 2022). By reducing the demand for virgin materials and

goods, the circular economy can effectively reduce the carbon footprint

associated with resource extraction, manufacturing, and transportation

(Kirchherr et al., 2018). The circular economy can also reduce emis-

sions from waste management practices such as landfilling and inciner-

ation. Urban expansion can significantly facilitate the transition to a

circular economy (Kurniawan et al., 2023). By implementing sustain-

able development practices like green building design and public trans-

portation, cities can reduce their carbon footprint and promote the

reuse and recycling of materials and goods(Yuan et al., 2006). Using

sustainable building practices, including using recycled materials and

energy-saving technologies, can effectively reduce the carbon footprint

associated with building development and usage. Similarly, using pub-

lic transit can lessen the need for personal vehicles, resulting in lower

transportation-related emissions and more effective resource use

(Gallego-Schmid et al., 2020).

Urban growth and the circular economy are mutually reinforcing.

For example, developing recycling and composting infrastructure in ur-

ban areas enables the reuse and recycling of materials and products,

opening up new possibilities (Yunan et al., 2021). Similarly, the circu-

lar economy contributes to sustainable urban development by reducing

the reliance on resource-intensive practices such as constructing new

buildings(Morseletto, 2020a). To fully realize the benefits of the circu-

lar economy for urban development and carbon emissions reduction,

policymakers and businesses must adopt a holistic approach to urban

planning and infrastructure development(Herrador et al., 2022). This

approach should prioritize energy-efficient construction, accessible

public transit, and the establishment of waste reduction and recycling

infrastructure.

Furthermore, companies and policymakers should support the tran-

sition to a circular economy through investment and policy measures

that encourage the creation of circular business models (Pires da Mata

Costa et al., 2021). Urbanization, carbon pollution, and the circular

economy are interconnected. According to Keijer et al. (2019), the cir-

cular economy promotes significant carbon emissions reduction in the

context of urban expansion by emphasizing the reuse, repair, and recy-

cling of goods and materials. Urban development can contribute to the

transition to a circular economy by embracing sustainable development

practices and creating resource and goods reuse and recycling opportu-

nities. Therefore, policymakers and businesses must prioritize sustain-

able development strategies and actively support the transition to a cir-

cular economy(Magazzino and Falcone, 2022).

Hypothesis 3. (H3). Carbon emissions and Urban development are

highly mediated by each other.

3.2.2. Urbanization, industrial rationalization, and carbon emissions

Industrial rationalization involves streamlining operations and opti-

mizing resource usage to enhance productivity and minimize wastage

(Stahel, 2019). In contrast, the circular economy promotes reusing, re-

pairing, and recycling goods and materials to mitigate pollution and re-

source consumption(Kirchherr et al., 2018). Embracing circular econ-

omy principles can lead to industrial rationalization, significantly re-

ducing carbon emissions. These concepts are closely interconnected,

and several methods of industrial rationalization can align with the car-

bon emissions impact of the circular economy(Chen et al., 2019). Indus-

trial rationalization reduces carbon emissions by streamlining produc-

tion processes and minimizing waste, thereby reducing the energy and

resources required for product and service manufacturing (M. A.-A.

CORRECTED PROOF

K. Di et al. Journal of Cleaner Production xxx (xxxx) 138436

Khan et al., 2023). In turn, it diminishes the carbon footprint associated

with resource extraction, production, and transportation.

Additionally, industrial rationalization facilitates the reuse and re-

cycling of goods and materials, which are fundamental aspects of the

circular economy. By optimizing production methods, businesses can

minimize waste and maximize material utilization (Keijer et al., 2019).

It reduces carbon emissions linked to resource extraction and manufac-

turing, decreasing the demand for virgin resources while increasing the

availability of recycled materials. Furthermore, industrial rationaliza-

tion facilitates the transition to renewable energy sources such as solar

and wind power. By reducing energy consumption and improving effi-

ciency, industrial rationalization contributes to adopting renewable en-

ergy (Wijkman and Skånberg, 2015). This significant shift in energy

sources can lead to substantial reductions in carbon emissions associ-

ated with energy generation and utilization.

Industrial rationalization is crucial in supporting the circular econ-

omy and directly influencing carbon emissions by providing businesses

with resource access and promoting sustainable growth(H. Wang et al.,

2020). Businesses can reduce costs and enhance profitability by opti-

mizing their manufacturing processes, creating a compelling case for

embracing circular economy principles (Geisendorf and Pietrulla,

2018). This optimization can stimulate creativity, open new markets,

and foster innovative business models for reusing and recycling materi-

als and goods. Furthermore, adopting circular economy principles can

generate economic opportunities for businesses by encouraging the de-

velopment of circular supply chains, where materials and products are

reused and recycled throughout the production and consumption cycle

(Suárez-Eiroa et al., 2021). It promotes economic growth and enhances

industry resilience and sustainability while mitigating the risks associ-

ated with supply chain disruptions. By optimizing production processes

and reducing waste, industrial rationalization can substantially de-

crease the carbon footprint associated with resource extraction, manu-

facturing, and transportation(Camacho-Otero et al., 2018). Addition-

ally, it facilitates the adoption of circular economy principles by provid-

ing businesses with resource access and fostering sustainable growth.

Policymakers and businesses must prioritize adopting circular economy

concepts and actively support transitioning to a more circular and effi-

cient economy.

Hypothesis 4. (H4).The circular economy and carbon emissions are

mediated mainly by industrial rationalization.

3.2.3. Carbon emissions, urban development, and the industrial resource

cycle

The term “Industrial Resource Cycle”(IRC) describes the closed-

loop system of material and energy fluxes within an industrial ecosys-

tem(Norman et al., 2006). The IRC aims to optimize resource efficiency

and minimize waste by recovering and reusing resources such as mate-

rials and energy. By facilitating the transition to a more sustainable and

circular economy, the IRC plays a significant role in mediating the rela-

tionship between the circular economy and carbon emissions (Fang et

al., 2017). One practical approach employed by the IRC to mitigate the

impact of the circular economy on carbon emissions is the recovery and

reuse of materials. Throughout production, the IRC promotes the recov-

ery and reuse of materials to reduce waste and the demand for new re-

sources (H. Dong et al., 2013). By minimizing the quantity of waste gen-

erated, the IRC reduces the carbon footprint associated with waste dis-

posal in landfills. The IRC can reduce the energy required for mining

and processing virgin resources by recovering and reusing materials,

thereby decreasing the carbon emissions associated with material ex-

traction and processing. Energy recovery and reuse represent another

means by which the IRC can mitigate the impact of the circular econ-

omy on carbon pollution (Murray et al., 2017). The IRC employs en-

ergy-efficient devices and the collection and repurposing of waste en-

ergy to promote energy recovery and reuse. By recovering and reusing

energy, the IRC can reduce the demand for energy from fossil fuel

sources, significantly contributing to carbon emissions(Morseletto,

2020a).

Furthermore, the IRC promotes using sustainable energy sources

such as solar and wind power, further reducing carbon emissions asso-

ciated with energy production (Norman et al., 2006). Additionally, the

IRC fosters the establishment of circular supply networks that facilitate

the reuse and recycling of materials and goods throughout the produc-

tion and consumption cycle. Consequently, transportation-related car-

bon emissions can significantly reduce by minimizing the need to trans-

port goods and materials. By promoting the expansion of circular sup-

ply networks, the IRC enhances the resilience and sustainability of the

economy while reducing the risk of supply chain disruption. Moreover,

the IRC is crucial in establishing novel marketplaces, and business mod-

els centered around reusing and recycling materials and goods(Dong et

al., 2013). It fosters sustainable development and creates new business

opportunities. These innovative business models and marketplaces may

drive the emergence of new technologies and techniques that con-

tribute to additional reductions in carbon emissions. Junnila et al.

(2018) found that the Industrial Resource Cycle can significantly miti-

gate carbon pollution and bridge the IRC and the circular economy gap.

By encouraging material and energy recovery and supporting circular

supply chains, new business models, and markets, the IRC can substan-

tially reduce the carbon footprint of industrial activities. Therefore,

businesses and policymakers must prioritize the implementation of the

IRC to expedite the transition to a more efficient and sustainable econ-

omy (L. Dong et al., 2017). Based on the above analysis, the mechanism

analysis of the digital economy and carbon emission is shown in Fig. 2.

Hypothesis 5. (H5).The Industrial Resource Cycle is crucial in mediat-

ing between carbon emissions and the circular economy.

4. Methodology and data

To systematically analyze the development of China's green, low-

carbon circular economy, examine the synergistic effects of spatial in-

homogeneity and carbon emission reduction, and comprehensively

consider the systematic effects involving multiple elements, it is neces-

sary to address potential confounding variables that may affect the rela-

tionship between the independent and dependent variables

(Geissdoerfer et al., 2017). Multiple regression methods establish a

baseline regression model, a standard hypothetical model used in vari-

ous research fields, including economics, social sciences, and natural

sciences(Kirchherr et al., 2017). By introducing these confounding vari-

ables as control variables, the relationship between the independent

and dependent variables can be assessed more accurately(Palmer et al.,

2016). Additionally, interpreting the interaction effects provides in-

sights into the complex relationships among the independent variables

(Pavan et al., 2022). Statistical inference facilitates evaluating the

model fit, determining the significant effect of the independent vari-

ables on the dependent variable, and making reliable conclusions and

decisions through hypothesis testing, confidence intervals, and model

evaluation indicators(Stegmann et al., 2022).

4.1. Data sources

This paper utilizes data from the China Statistical Yearbook for 30

provinces in China spanning 2000–2019, based on China's gradual and

intensive promotion of green development since 2000. They provided a

stage of exploration and gradual maturity for green and low-carbon de-

velopment. This significant period encompasses vital economic reform

and development stages and various social, economic, and political

changes. Analyzing this timeframe allows researchers to assess the im-

pact of these changes. Due to the unavailability of data for 2020–2021

for several variables examined in this study, they included. The data un-

dergo the following treatment: exclusion of missing values for

provinces and years, natural logarithm transformation of certain con-

CORRECTED PROOF

K. Di et al. Journal of Cleaner Production xxx (xxxx) 138436

Fig. 2. Analyses of the carbon emissions and the circular economy.

tinuous variables, and trimming of extreme values at the 1% and 99%

tails—a total of 15,495 observations obtained.

4.2. Description of variables

4.2.1. Explained variables

Carbon Emissions (CE): Carbon emissions refer to the release of car-

bon dioxide (CO2) and other greenhouse gases into the atmosphere

through various human activities(Herrador et al., 2022). These activi-

ties include burning fossil fuels, industrial production, transportation,

deforestation, and changes in land use(Santagata et al., 2020). Carbon

emissions are a significant contributor to global climate change. The

emission of greenhouse gases enhances the greenhouse effect in the at-

mosphere, increasing the Earth's surface temperature(De Souza and

Pacca, 2021). In turn, it impacts the climate system and leads to various

issues, such as extreme weather events and rising sea levels(Smol and

Koneczna, 2021). Monitoring and controlling carbon emissions is very

important to national and international organizations. Many countries

have established targets and implemented measures to reduce green-

house gas emissions, including enhancing energy efficiency, increasing

the use of renewable energy, developing clean technologies, and im-

proving carbon emissions management in industry and transportation

sectors(Kumar Awasthi et al., 2022).

Carbon Intensity (CI): Carbon intensity measures the carbon emis-

sions produced per unit of output or activity. It measures an economy or

activity's impact on carbon emissions(Pires Da Mata Costa et al., 2021).

Carbon intensity is typically calculated based on energy consumption,

GDP, or product output(Sherwood et al., 2022). A lower carbon inten-

sity indicates that economic activities or outputs are associated with

relatively fewer carbon emissions, while a higher carbon intensity indi-

cates a higher level of carbon emissions(Rashid and Shahzad, 2021).

Reducing carbon intensity is a common environmental goal to lower

carbon emissions and promote sustainable development(Zhou et al.,

2019).

4.2.2. Explanatory variables

Circular Economy (CEc): The circular economy is an economic

model that aims to achieve economic growth and sustainable develop-

ment by minimizing resource consumption and waste generation and

by promoting the recycling and reuse of resources and products within

the economic system(Brändström and Saidani, 2022). It involves shift-

ing from a linear “take-make-dispose”model to a circular approach that

treats waste as a valuable resource(Bloise, 2020). The circular economy

emphasizes extending the lifespan of resources, reducing the demand

for new resources, and promoting recycling, reuse, and reclamation

(Saidani et al., 2019). Existing literature suggests several indicators for

measuring the circular economy. These include the waste reuse rate,

which measures the proportion of recycled and reused waste, including

material reuse and energy recovery(Vinante et al., 2021). The material

recycling rate measures the extent to which materials are recycled and

reused within the circular economy relative to the total material re-

quirements(Moraga et al., 2019b). Energy efficiency is another indica-

tor that measures energy use efficiency in economic activities, includ-

ing the relationship between energy consumption and output. Green

jobs, on the other hand, measure the number and quality of jobs created

in environmentally friendly industries and occupations within the cir-

cular economy(Negrete-Cardoso et al., 2022).In this paper, a compre-

hensive indicator system is constructed based on expert indicators re-

lated to the material cycle, energy cycle, value cycle, and other aspects

to measure the level of national circular economy development(Goyal

et al., 2021). The specific secondary indicators, their meanings, and

their attributes detailing in Table 1. The entropy weighting method us-

ing to determine the weights of each secondary indicator, followed by

the TOPSIS evaluation method to comprehensively measure the level of

circular economy development in each of China's 30 provinces. The

specific calculation steps are as follows.

①Data normalization: The initial data matrix is

normalized to obtain matrix .If j is a positive

indicator, it is processed according to equation (1). If j is a

negative indicator, it is processed according to equation (2). To

ensure the feasibility of subsequent calculations, let .

(1)

(2)

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K. Di et al. Journal of Cleaner Production xxx (xxxx) 138436

Table 1

Comprehensive index measurement methodology for the national circular economy.

Primary index Secondary indicators Indicator

weights Data

source N Indicator

attributes

Material-cycles

indicators Main resource output rate (yuan/ton) 0.076 CSY 600 Positive

Recycling rate of main waste (%) 0.019 CSY 598 Positive

Energy-cycles

indicators Energy output rate (million yuan/ton of standard

coal) 0.233 CSY 569 Positive

Water output rate (yuan/ton) 0.093 CSY 589 Negative

Construction land output rate (million yuan/ha) 0.020 CSY 596 Positive

General industrial solid waste comprehensive

utilization rate (%) 0.073 CSY 597 Positive

Repeated water consumption rate of industrial

enterprises above scale(%) 0.043 CSY 592 Negative

Major recycled resources recovery rate (%) 0.233 CSY 594 Negative

Municipal construction waste resource treatment rate

(%) 0.006 CSY 586 Positive

Urban recycled water utilization rate (%) 0.007 CSY 587 Positive

Total output value of resource recycling industry

(billion yuan) 0.053 CSY 596 Positive

Value-cycles

indicators Industrial solid waste disposal volume (billion tons) 0.036 CSY 591 Positive

Industrial wastewater emissions (billion tons) 0.024 CSY 563 Positive

Urban domestic waste landfill disposal volume (billion

tons) 0.027 CSY 592 Negative

Emissions of key pollutants - (billion tons) 0.058 CSY 568 Negative

②Calculate the weighting of each indicator :

(3)

③Calculate the entropy value of each indicator, where the entropy

value for the jth indicator is:

(4)

④Calculate the weight of the jth indicator based on the entropy value

of each indicator:

(5)

⑤The decision matrix is calculated from the normalization

matrix and equation (5):

(6)

⑥Determine the positive ideal solution and the negative ideal

solution for each indicator:

(7)

(8)

Calculate the distance between the positive ideal solution and the

negative ideal solution for each indicator:

(9)

(10)

⑧The ideal solution for each object of the technique is posted into

(11)

Where indicates the closeness of each object to the optimal target,

the larger the value, the better the development of the circular econ-

omy, while the opposite indicates a relatively slow development of the

circular economy.

4.2.3. Control variables

In conjunction with the previous literature review, “industrial struc-

ture”refers to the distribution and composition of various economic in-

dustries(X. Zhang and Liu, 2022). Implementing a circular economy can

influence the development of different industries(X. Zhang and Liu,

2022). By controlling for changes in the industrial structure, it is possi-

ble to eliminate inter-industry variations that may interfere with the re-

lationship between the circular economy and carbon emissions

(Gebhardt et al., 2022). Considering the close relationship between in-

dustrial structure, resource utilization, energy consumption, and car-

bon emissions, it is reasonable to incorporate industrial structure when

studying the circular economy(Morseletto, 2020b). A high level of in-

dustrialization indicates that a significant proportion of the national

economy comprises the industrial sector. The degree of industrializa-

tion can impact implementation of a circular economy and carbon

emissions(Negrete-Cardoso et al., 2022). Higher levels of industrializa-

tion often coincide with increased energy consumption and carbon

emissions. Thus, controlling for high levels of industrialization makes it

possible to study the impact of a circular economy on carbon emissions

without the interference of industrialization levels(Goyal et al., 2021).

The term “dominant industrial structure”refers to industries with a

dominant position within an economic system. Different dominant in-

dustry structures may have varying effects on implementing a circular

CORRECTED PROOF

K. Di et al. Journal of Cleaner Production xxx (xxxx) 138436

economy and carbon emissions(X. Zhang and Liu, 2022). By controlling

for changes in the dominant industrial structure, a more accurate study

of the impact of the circular economy on carbon emissions can conduct

while also excluding interference from the dominant industrial struc-

ture(Gebhardt et al., 2022). These control variables are selected based

on a comprehensive analysis and theoretical study of the relationship

between the circular economy and carbon emissions(Morseletto,

2020b). Their inclusion aims to enhance the accuracy and interpretabil-

ity of the study.

Industrial Structure (ISe): The term “industrial structure”refers to

the composition and relative significance of various industrial sectors

within a region, country, or economy(K. Khan et al., 2022). It provides

insights into the relative size and importance of different economic in-

dustries. The Industrial Structure Index (ISe) is a standard measure to

assess the industrial structure. This index reflects the relative share of

each industry in terms of total output or employment(Ormazabal et al.,

2018b). Analyzing changes in the Industrial Structure Index helps to

understand the development and transformation of different industrial

sectors within a region or country(Tao et al., 2019). Industrial classifi-

cation systems such as the International Standard Industrial Classifica-

tion (ISIC) or the North American Industry Classification System

(NAICS) are employed for detailed classification and comparative

analysis of industrial structures. In addition to ISIC and NAICS, this

study utilizes expert-recommended indicators such as the proportion of

employment in industry, the proportion of value added in industry, and

the proportion of exports to measure the industrial structure. These in-

dicators provide valuable information on the relative importance and

contribution of different industries to the economy(Hertwich, 2021).

High Industrialization (HId): High industrialization refers to a situa-

tion where a region or country has a significant share of the industrial

sector, resulting in substantial industrial output and employment op-

portunities(Xue et al., 2019). This condition is typically characterized

by a higher proportion of the industrial sector's contribution to the

gross domestic product (GDP), a more significant of individuals em-

ployed in the industry, and a more prominent position of industrial out-

put within the national economy(Pavan et al., 2022). Value Added of

Industry as a Share of GDP: This indicator measures the extent to which

the industry contributes to the GDP. A higher share of industrial value

added to GDP signifies a more industrialized economy(D. Dong et al.,

2020). Share of Industrial Employment in Total Employment: This indi-

cator, as suggested by the experts in this study, assesses the proportion

of employment in the industrial sector compared to total employment

(Liu and Xin, 2023). It provides a comprehensive assessment of a region

or country's level of industrialization and degree of industrial employ-

ment.

Dominant Industry Structure (DIs): The term “dominant industry

structure”refers to the industries or sectors that hold a dominant or

leading position within an economy(Awan et al., 2021). Assessing the

structure of dominant industries involves identifying the key industries

or sectors that play a significant role in the national economy and eval-

uating their contribution and influence using various indicators

(Grafström and Aasma, 2021). These indicators encompass a range of

methods, such as employment figures, exports and trade data, output

value, and contribution margin(Mgbechidinma et al., 2022). In this

study, the industry chain linkage method commonly employed by ex-

perts is utilized, which examines the close relationships between the

leading industry and other industries within the supply and value

chains(Van Doren et al., 2018). By analyzing these interconnections, it

becomes possible to identify the industries that exert a leading influ-

ence on the overall economic system.

Energy Mix (ESt): The energy mix refers to the proportion and com-

position of different energy sources used in the energy consumption of a

region or country(Vujanović et al., 2022). It provides insights into the

characteristics and preferences of an economy or society regarding en-

ergy utilization(Christensen, 2021). Measuring the energy mix involves

considering several indicators, such as the proportion of various energy

sources in total energy consumption. Familiar energy sources include

oil, natural gas, coal, nuclear power, and renewable energy

(Mgbechidinma et al., 2022). By comparing the consumption propor-

tions of each energy source, it becomes possible to assess the composi-

tion and characteristics of the energy mix(Van Doren et al., 2018). This

study uses energy consumption as the indicator commonly recom-

mended by experts. Energy consumption represents the amount con-

sumed from various sources from energy statistics or survey reports.

Analyzing the magnitude and changes in energy consumption allows

for identifying shifts and trends in the energy mix.

4.2.4. Mediating variables

Urban Development (URd): Urban development is influenced by

various factors, including population growth, economic progress, tech-

nological advancements, and evolving social and cultural norms. Effec-

tive urban development management is crucial to ensure its positive

impact on communities(Xue et al., 2019). When properly planned and

implemented with sustainability in mind, urban development can bring

numerous benefits, such as improved service accessibility, enhanced

living conditions, expanded economic opportunities, and reduced envi-

ronmental impact(Tauseef Hassan et al., 2023). However, poorly

planned development can lead to urban sprawl, traffic congestion, pol-

lution, social inequality, and other negative consequences.

Industrial Rationalization (IRn): Industrial rationalization refers to

optimizing industrial operations and enhancing efficiency through

technological advancements and other measures(Erythropel et al.,

2018). The primary objective of industrial rationalization is to reduce

costs, increase productivity, and improve competitiveness by eliminat-

ing wasteful practices, streamlining processes, and optimizing resource

utilization(Van Doren et al., 2018). Various approaches can be em-

ployed for industrial rationalization, including automation, lean pro-

duction, and supply chain optimization (Jacobi et al., 2017). These ini-

tiatives often rely on data analysis and analytics to identify areas for im-

provement and monitor performance progress over time(Poveda et al.,

2019).

Industrial Resource Cycle (IRc): The industrial resource cycle en-

compasses strategies and processes to optimize resource utilization and

minimize waste in industrial production(Tauseef Hassan et al., 2023). It

emphasizes the principles of reuse, recycling, and resource recovery to

promote a more sustainable and circular economy, thereby reducing

the consumption of finite resources and minimizing waste generation

(Grafström and Aasma, 2021). The industrial resource cycle encom-

passes various practices, including closed-loop production systems, re-

manufacturing, and waste-to-energy technologies(Lehmann et al.,

2022). By implementing these practices, the environmental impact of

industrial production can minimize, cost savings can achieve, and new

revenue streams can generate through the recovery and reuse of materi-

als.

Based on the preceding analysis, Fig. 2 illustrates the framework for

establishing the baseline regression model presented in this paper

(Fuinhas et al., 2021).

This study investigated a sample of 30 Chinese provinces, utilizing

detailed data from 2000 to 2019. Linear interpolation was employed as

the method for data interpolation. Most primary data in this research

were obtained from publicly accessible official sources, including rele-

vant indicators from databases and national statistics yearbooks. De-

scriptive statistics for all the data are presented in Table 2.

In order to make the graphics appear more visual and accurate, in

addition to the circular economy data and the other nine data variables,

we visualized each of our variables through data visualization analysis;

as shown in Fig. 3, we can present the data for each variable.

CORRECTED PROOF

K. Di et al. Journal of Cleaner Production xxx (xxxx) 138436

Table 2

Variable descriptive statistics.

Variable N Mean SD Min Max Data sourc

CE 597 10.00 0.905 6.307 11.93 CSY

CI 597 1.911 0.677 −0.372 3.800 CSY

CEc 600 0.277 0.146 0.0410 0.827 CSY

URd 600 5.688 0.962 1.386 8.115 CSY

ISe 600 −0.0310 0.507 −1.655 1.239 CSY

HId 600 10.09 0.839 7.923 11.99 CSY

DIs 600 0.0540 0.365 −0.654 1.654 CSY

ESt 600 0.258 0.731 −1.609 1.795 CSY

IRn 599 5.311 1.016 0.001 6.312 CSY

IRc 592 3.523 0.901 −0.416 5.290 CSY

4.3. Model setting

Based on the previous analysis, the benchmark regression model set

in this paper is shown in equation (12).

(12)

The explanatory variable represents either carbon emissions or

carbon intensity. The core explanatory variable corresponds to the

circular economy development index of province i in year t, while

represents a set of control variables. Moreover, serves as

an individual fixed effect to account for interactions among different

provinces. Year is included as a time fixed effect to adjust for exogenous

effects at the macro level, and Province is used as a province fixed effect

to control for exogenous effects at the province level.In particular:

(13)

(14)

In the equation above: 、∼denotes the coefficient of each

variable. denotes the constant term. and denote the individual

fixed effects and time fixed effects, respectively. denotes the random

error term.

Based on our previous discussion of transmission mechanisms, the

circular economy can potentially affect carbon emissions through the

mediating variables of urban development (URd), industrial rational-

ization (IRn), and industrial resource cycle (IRc).The indirect effects of

independent variables on dependent variables through intermediate

variables are commonly referred to as mediating effects (MacKinnon et

al., 2000).To examine whether the aforementioned factors can function

as mediating variables, additional empirical analysis was performed

utilizing a standardized mediating effects model and the Sobel test.

(15)

(16)

(17)

The standardised mediating effects model is defined by Equations

(15)–(17), with X representing the explanatory variable, Y representing

the explained variable, and M representing the mediating variable.The

hypotheses numbered three, four, and five were evaluated using the fol-

lowing methods.Equations (18), (19), and (20), which are associated

with Equation (15), provide regression equations that describe the rela-

tionships between the circular economy and the mediating variables:

urban development, industrial rationalization, and industrial resource

cycle.Equation (17), which considers the mediating variables (23)(15),

is equivalent to Equations (21)–(23).

(18)

(19)

(20)

(21)

(22)

(23)

Table 3

The outcomes of benchmark regression are displayed.

Variables Static panel model (OLS) Dynamic panel model (GMM)

lnCE lnCI lnCE lnCI lnCE lnCI

lnCEc −0.444***

(-0.171)

−0.506***

(-0.185)

−0.696***

(-0.209)

−0.913***

(−0.22)

−0.378**

(0.157)

−0.350**

(−0.159)

lnURd 0.384***

(−0.105) 0.664***

(−0.103) 0.224 (−0.415) 0.474(-0.229)

lnIRn 0.0234***

(-0.00797) 0.0191**

(-0.00794) 0.00591

(-0.00621) 0.00276

(-0.00694)

lnIRc 0.0988***

(-0.0344) 0.0802**

(-0.0314) 0.00222

(-0.0173)

−0.0255

(-0.0174)

L.CE 0.703***

(-0.218)

L.CI 0.871***

(-0.192)

Constant 8.878***

(-0.131) 1.498***

(-0.141) 5.306***(-1.079) −4.610***

(-1.07)

Controlvariables NO NO YES YES YES

Year FE YES YES YES YES YES YES

Country FE YES YES YES YES YES YES

R-squared 0.957 0.91 0.963 0.936

N 595 595 582 582 553 553

Note:***p < 0.01, **p < 0.05, *p < 0.1.

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K. Di et al. Journal of Cleaner Production xxx (xxxx) 138436

Fig. 3. Visual analysis chart of data for each variabl

5. Analysis and results

5.1. Determined stages of the circular economy's growth

The entropy value technique was utilized in this study to assess the

level of circular economy development in 30 provinces. Fig. 4 illus-

trates a positive correlation between policy improvement efforts, re-

gional economic growth, and the development of the circular economy

(AliAkbari et al., 2021). Various industrial parks nationwide have been

established with the circular economy as a central focus, such as the

Tianjin Binhai New Area Circular Economy Park and the Guangdong

Provincial Circular Economy Park. China has made significant strides in

developing industrial parks dedicated to resource recycling, including

the Tianjin Binhai New Area Circular Economy Park and the Guang-

dong-Hong Kong-Macau Greater Bay Area Circular Economy Industrial

Park. Considering the regional growth level and the effectiveness of ap-

proved policies, the Western region exhibits potential for more robust

circular economy development. Nonetheless, the overall level of devel-

opment in China falls between medium and high, laying a solid founda-

tion for establishing a high level of development in a low-carbon circu-

lar economy(Gallego-Schmid et al., 2020).

China, which has made remarkable progress in the circular econ-

omy, has implemented numerous policies and programs to support its

growth. It has established multiple circular economy industrial parks

and enacted laws and regulations to promote the development of green

firms and waste recycling(Kirchherr et al., 2017). The country has also

allocated substantial funds for research and development of cutting-

edge technologies. Nevertheless, there are challenges in implementing

a circular economy in China, such as addressing issues like inadequate

waste management infrastructure and providing incentives for busi-

nesses to adopt circular economy practices. Despite these challenges,

China has made significant strides in circular economy growth in recent

years. The nation's ongoing efforts in this domain are expected to con-

tinue playing a pivotal role in advancing sustainable development.

5.2. Benchmark regression findings

This study examines the impact of the circular economy on carbon

emissions. A baseline regression analysis is conducted to account for

macroeconomic and behavioral characteristics that remain constant

over time. The findings indicate that expanding the circular economy

has a detrimental effect on carbon emission intensity. The coefficient

for the circular economy variable is statistically significant at the 5% or

1% level. Additionally, a first-order lag term of the dependent variable

is included in the analysis.

The results of the systematic generalized method of moments

(GMM) estimation demonstrate that the growth of the circular economy

leads to a reduction in both the scale and level of carbon emissions. The

correlation coefficients between energy mix, carbon emissions, and car-

bon emissions per capita in a circular economy exhibit negative and sta-

tistically significant associations at the 1% level. Furthermore, stricter

government regulations on carbon emissions are a powerful instrument

for achieving carbon reduction targets before establishing a market-

based emission reduction system. The coefficients related to govern-

ment intervention are substantial at the 1% level. Moreover, the level of

urbanization significantly contributes as an explanatory variable at the

1% level, and carbon emissions and intensity per capita are strongly in-

versely associated with external openness.

CORRECTED PROOF

K. Di et al. Journal of Cleaner Production xxx (xxxx) 138436

Fig. 4. Average level of circular economy development in 30 Chinese provinces from 2000 to 2019.

5.3. Robustness tests

Upon examining the complete dataset, we observed notable varia-

tions in the results when considering different periods. To verify the ro-

bustness of our findings, we conducted additional tests by halving the

time duration. The results of these robustness tests for the rescaled sam-

ple intervals from 2001 to 2019 are presented in Table 4. We also elimi-

nated the outliers to mitigate their potential influence before conduct-

ing the robustness tests. Subsequently, Table 5 presents the results of

the robustness tests after removing the extreme values. The ordinary

least squares (OLS) regression results indicate that even after adjusting

the sample intervals and excluding the extreme values, the correlation

coefficients between the rate of circular economy expansion and carbon

emissions remain negative.

However, the correlation coefficients for carbon emissions per

capita are consistently negative. Moreover, the estimation results for

the control variables exhibited minimal variation, aligning closely with

those of the initial regression analysis. These findings provide confi-

dence in the reliability of the data collected during this investigation.

5.4. The circular economy's indirect impact on carbon emissions

The study investigated the mediating effects of urbanization, indus-

trial rationalization, and the industrial resource cycle on the relation-

ship between circular economy growth and carbon emissions. These

variables establish a connection between the circular economy and car-

bon emissions based on their level of development. The Sobel test as-

sessed each variable's role as a mediator.

5.4.1. Investigating how urbanization affects how disputes are resolved

The study investigated the mediating role of urban growth in the re-

lationship between the circular economy and carbon emissions. The

Table 4

Results of robustness tests (Sample interval 2001–2019).

Variables Panel-stiffness model (OLS) Interactive panel model (GMM)

lnCE lnCI lnCE lnCI lnCE lnCI

lnCEc −0.398**

(-0.169)

−0.419**

(-0.184)

−0.593***(-0.2) −0.770***

(-0.217)

−0.503**

(-0.225)

−0.361(-0.22)

lnURd 0.357***(-0.105) 0.635***(-0.102) 0.31(-0.427) 0.535*(-0.27)

lnIRn 0.0256***

(-0.00759) 0.0213***

(-0.00757) 0.00846

(-0.00659) 0.0052

(-0.00675)

lnIRc 0.0934***

(-0.0355) 0.0768**(-0.032) 0.000579

(-0.016)

−0.0227

(-0.0168)

L.CE 0.686***

(-0.222)

L.CI 0.809***

(-0.209)

Constant 8.878***

(-0.131) 1.498***

(-0.141) 5.306***(-1.079) −4.610***(-1.07)

Controlvariables NO NO YES YES YES YES

Year FE YES YES YES YES YES YES

Country FE YES YES YES YES YES YES

R-squared 0.957 0.91 0.963 0.936

Observations 565 565 554 554 525 525

Note:***p < 0.01, **p < 0.05, *p < 0.1.

CORRECTED PROOF

K. Di et al. Journal of Cleaner Production xxx (xxxx) 138436

Table 5

Results of the robustness test after removing extreme values.

Variables Panel-stiffness model (OLS) Interactive panel model (GMM)

lnCE lnCI lnCE lnCI lnCE lnCI

lnCEc −0.444***

(-0.171)

−0.506***

(-0.185)

−0.696***

(-0.209)

−0.913***

(-0.22)

−0.378**

(-0.157)

−0.330**

(-0.156)

lnURd 0.384***(-0.105) 0.664***(-0.103) 0.224(-0.415) 0.453*(-0.242)

lnIRn 0.0234***

(-0.00797) 0.0191**

(-0.00794) 0.00591

(-0.00621) 0.00276

(-0.00694)

lnIRc 0.0988***

(-0.0344) 0.0802**

(-0.0314) 0.00222

(-0.0173)

−0.0255

(-0.0174)

L.CE 0.703***

(-0.218)

L.CI 0.873***

(-0.194)

Constant 8.878***

(-0.131) 1.498***

(-0.141) 5.306***(-1.079) −4.610***

(-1.07)

Controlvariables NO NO YES YES YES YES

Year FE YES YES YES YES YES YES

Country FE YES YES YES YES YES YES

R-squared 0.957 0.91 0.963 0.936

Observations 597 597 589 589 499 499

Note: ***p < 0.01, **p < 0.05, *p < 0.1.

Table 6

Verifying the mechanism (Urban development as a stepping stone variable).

Variables M = lnURd/Explained Variable = lnCE M = lnURd/Explained Variable = lnCI

(1) (6) (9) (1) (6) (9)

lnCEc −0.521**

(-3.15) 0.400***(5.71) −0.712***

(-4.24)

−0.561**

(-3.24) 0.400***(5.71) −0.865***

(-5.07)

M 0.454***(-4.54) 0.727***(-7.15)

_cons 9.389***

(60.00) 10.29***

(154.13) 4.721*** (4.54) 2.151***

(13.16) 10.29***

(154.13)

−5.315***

(-5.03)

R2 0.96 0.99 0.96 0.93 0.99 0.93

Adj R2 0.96 0.99 0.96 0.92 0.99 0.93

Control

variables YES YES YES YES YES YES

Year FE YES YES YES YES YES YES

Country FE YES YES YES YES YES YES

N 567 570 567 567 570 567

Note:***p < 0.01, **p < 0.05, *p < 0.1.

findings revealed a negative impact of the circular economy on carbon

emissions, as evidenced by an estimated coefficient of −0.521 when

carbon intensity was used as the dependent variable. Developing a cir-

cular economy can effectively reduce carbon intensity. Additionally,

the study found that the circular economy has a detrimental effect on

urban development, decreasing carbon intensity. When considering ur-

ban expansion as a mediating variable, the regression coefficient for the

circular economy's impact on carbon intensity increased from −0.521

to −0.712. It indicates that the growth of the circular economy signifi-

cantly supports urban development while simultaneously reducing car-

bon intensity, with a mediating impact of 54.3%. Moreover, the circular

economy negatively affects carbon emissions per person, with an esti-

mated coefficient of −0.561. Holding all other factors constant, a 1% in-

crease in the expansion of the circular economy results in a 0.561% re-

duction in carbon emissions per person.

Furthermore, the study revealed that promoting urban growth while

increasing carbon emissions per person can lower carbon intensity. This

finding suggests that allocating more resources to energy efficiency

Table 7

Verifying the mechanism (Industrial Rationalization as a stepping stone variable).

Variables M = lnIRn/Explained Variable = lnCE M = lnirn/Explained Variable = lnCI

(1) (7) (10) (1) (7) (10)

lnCEc −0.521**(-3.15) −1.910*(-2.22) −0.427* (−2.57) −0.561**(-3.24) −1.910* (−2.22) −0.471** (−2.70)

M 0.0238** (−2.87) 0.0204*(-2.36)

_cons 9.389***(60.00) 6.918*** (8.47) 9.193*** (55.21) 2.151***(13.16) 6.918***(8.47) 1.977***(11.34)

R20.96 0.92 0.96 0.93 0.92 0.93

Adj R20.96 0.85 0.96 0.92 0.86 0.92

Control variables YES YES YES YES YES YES

Year FE YES YES YES YES YES YES

Country FE YES YES YES YES YES YES

N 567 569 566 567 569 566

Note:***p < 0.01, **p < 0.05, *p < 0.1.

CORRECTED PROOF

K. Di et al. Journal of Cleaner Production xxx (xxxx) 138436

measures and developing new technologies becomes crucial as urban-

ization and carbon emissions increase. The research findings support

that the circular economy's impact on urban growth and carbon inten-

sity can significantly reduce overall carbon emissions.

5.4.2. Examination of the mediating effects of industrial rationalization

Equation (1) presents the overall impact, while Equations (7) and

(10) depict the stepwise regression test for the mediating effect. When

Industrial Rationalization is included as a mediating variable, the re-

gression coefficient of the circular economy on carbon emission inten-

sity decreases from −0.521 to −0.427. It indicates that increasing In-

dustrial Rationalization can reduce carbon emission intensity.

Based on the Sobel test, the mediating effect accounts for 6.2% of

the variance. The digital economy shows the most positive impact on

carbon emissions per person, with an estimated coefficient of 0.842. In

Equation (10), when considering the circular economy and industrial

rationalization advances, the expected effect of the circular economy on

carbon emissions per capita declines from −0.561 to −0.471. By posi-

tively influencing Industrial Rationalization, the circular economy can

increase carbon emissions per person, ultimately leading to higher

overall carbon emissions.

Equation (10) reveals that Industrial Rationalization has a regres-

sion coefficient of −0.561 on per capita carbon emissions, and the Sobel

test indicates that the mediating effect accounts for 12.8% of the total

impact. Industrial Rationalization plays a crucial role in promoting in-

novation by diversifying risks and providing funding for technological

advancements. Moreover, internet finance indirectly affects firms' in-

vestment in technological innovation by efficiently transmitting infor-

mation on monetary demand and supply.

5.4.3. Analysis of the industrial resource Cycle's mediating impacts

Table 8 presents the expected results of the mediating impact using

the Industrial Resource Cycle as the mediating variable. The findings

from Equations (1), (8) and (11) align with those discussed in the pre-

vious section. Carbon emissions are projected to be influenced by the

circular economy, with a forecasted value of −0.521. When the indus-

trial structure is included as a mediating variable, the regression coeffi-

cient of the circular economy on carbon emission intensity increases

from −0.521 to −0.482. The circular economy can reduce carbon emis-

sion intensity by positively modifying the industrial structure. For

every 1% increase in the expansion of the circular economy, there is an

average improvement of 1.305% in the industrial structure. The circu-

lar economy significantly impacts the mediating variable (industrial

structure) and the economy as a whole. According to the Sobel test, the

mediating effect accounts for 15.8% of the variation.

When considering the circular economy and upgrades in the indus-

trial structure, the estimated coefficient of the circular economy on car-

bon emissions per capita changes from 0.561 to 0.525. It demonstrates

that the circular economy's positive effects on the industrial structure

can increase carbon emissions per capita. Equation (10) also reveals a

regression coefficient of −0.245 between the increase in the industrial

structure and per capita carbon emissions. The Sobel test results suggest

that the mediating effect explains 38.6% of the variance. The circular

economy promotes industry disruption, leading to increased output,

lower costs, economies of scale, efficient distribution, and innovation,

all of which contribute to the modernization of the industrial structure.

5.5. Heterogeneity test

To further enhance the feasibility of the data analysis, we classified

the provinces into three regions: East, West, and Central. The results ob-

tained from analyzing the geographical heterogeneity are presented in

Table 9. The findings reveal significant variations in the practical path-

ways of circular economy development between the East and West re-

gions. These differences provide new insights and highlight the policy

requirements for promoting synergistic development between China's

East and West regions. It is crucial to consider local conditions and ad-

dress the East-West disparity while implementing the rapid develop-

ment of the circular economy. By formulating appropriate policies and

strategies tailored to each region, we can foster the synergistic develop-

ment of China's East-West regions and facilitate the advancement of the

circular economy to a higher level.

The circular economy is a pivotal driver of sustainable development

in the future, requiring region-specific strategies to address geographi-

cal heterogeneity and promote its implementation. Through a compre-

hensive analysis of the East, West, and Central regions, we can formu-

late targeted circular economy policies and pathways that align with lo-

cal contexts and foster synergistic development between the East and

West, thus advancing China's circular economy process.

In the Eastern region, the focus should be on developing new energy

and high-technology industries, establishing and enhancing the circular

chain of production and consumption, and promoting efficient energy

and material utilization. Simultaneously, there is a need to bolster re-

search and application of environmental protection technologies in crit-

ical areas, improve the environmental sustainability of enterprise pro-

duction, and drive rapid circular economy development in the East.

In the Central region, emphasis should be placed on promoting the

comprehensive utilization of resources and optimizing and upgrading

industrial structures. Particular attention should be given to the devel-

opment of recycling agriculture, ecological agriculture, and other dis-

tinctive industries. Accelerating the integrated development of urban

and rural areas and implementing effective environmental protection

measures, including waste classification and treatment, will contribute

to developing a circular economy in the Central region.

In the Western region, the focus should be on harnessing and maxi-

mizing the potential of local renewable resources, such as solar and

wind energy, to establish and enhance the local circular economy sys-

tem. Concurrently, there is a need to develop ecological agriculture ac-

tively, integrating agriculture and animal husbandry with ecological

protection, to achieve a harmonious balance between resource preser-

Table 8

Verifying the mechanism (Industrial Resource Cycle as a stepping stone variable).

Variables M = lnIRc/Explained Variable = lnCE M = lnIRc/Explained Variable = lnCI

(1) (8) (11) (1) (8) (11)

lnCEc −0.521**(-3.15) −0.507*(-2.16) −0.482**(-2.90) 0.561** (−3.24) −0.507*(-2.16) 0.525**(-3.01)

M 0.0907**(-2.98) 0.0773*(-2.43)

_cons 9.389***(60.00) 3.091*** (13.79) 9.112*** (49.90) 2.151***(13.16) 3.091***(13.79) 1.913***(10.00)

R20.96 0.93 0.96 0.93 0.93 0.93

Adj R20.96 0.92 0.96 0.92 0.92 0.92

Control variables YES YES YES YES YES YES

Year FE YES YES YES YES YES YES

Country FE YES YES YES YES YES YES

N 567 565 563 567 565 563

Note:***p < 0.01, **p < 0.05, *p < 0.1.

CORRECTED PROOF

K. Di et al. Journal of Cleaner Production xxx (xxxx) 138436

Table 9

Analysis of circular economy heterogeneity in eastern, central and western regions.

Variables Eastern Central Western

lnCE lnCI lnCE lnCI lnCE lnCI

lnCEc −1.519***

(-0.198)

−1.678***

(-0.224) 1.077(-1.015) 0.846(-0.916) 1.606***(-0.433) 1.549***(-0.398)

lnURd 1.191***(-0.15) 1.668***(-0.169) 0.501**(-0.203) 0.525***

(-0.187)

−0.574**

(-0.225)

−0.261(-0.203)

lnIRn 0.0823**

(-0.0415) 0.0887*

(-0.0468)

−0.00948

(-0.048)

−0.0428

(-0.0469) 0.336***

(-0.0542) 0.326***

(-0.0491)

lnIRc −3.707(-2.963) −3.313(-3.411) −2.666(-6.355) −1.742(-6.067) −0.561***

(-0.176)

−0.494***

(-0.157)

Constant −1.591(-1.515) −13.41***

(-1.71) 5.538***

(-1.835)

−2.753(-1.701) 14.67***(-2.056) 4.256**(-1.869)

Controlvariables YES YES YES YES YES YES

Year FE YES YES YES YES YES YES

Country FE YES YES YES YES YES YES

Observations 198 198 178 178 213 213

R-squared 0.983 0.925 0.966 0.951 0.969 0.977

Note:***p < 0.01, **p < 0.05, *p < 0.1.

vation and sustainable utilization, thus promoting circular economy de-

velopment in the Western region.

Additionally, it is crucial to strengthen synergistic development be-

tween different regions by fostering exchanges and cooperation. This

approach will enhance complementarity within the circular economy

across regions and facilitate nationwide progress. Practical policy guid-

ance and pathways are pivotal in developing a circular economy. Tax

concessions, financial support, and industrial guidance can be adopted

to promote circular economy development. Moreover, efforts should be

dedicated to strengthening the training and recruitment of profession-

als in the field of circular economy, establishing a skilled workforce

proficient in technical and management aspects. Considering the het-

erogeneous analysis of the East, West, and Central regions, tailored

strategies for circular economy development based on local conditions

are essential. These strategies will facilitate synergistic development

between the East and West regions and promote the circular economy

process in China. By leveraging the circular economy's potential, we

can effectively drive economic development while ensuring ecological

and environmental protection, achieving efficient resource utilization,

reduced environmental pollution, improved energy efficiency, and de-

creased dependence on natural resources. Thus, it is imperative to rec-

ognize the significance of circular economy development and under-

take proactive measures to promote its adoption.

6. Conclusions

In conclusion, this study addresses the issue of spatial imbalances

and synergistic effects in carbon emission reduction, providing valuable

insights for policy-makers and industry practitioners. Integrated spatial

analysis and general systems theory, a comprehensive analysis exam-

ines differences, complementarities, and policy coordination in carbon

reduction efforts. The study's findings have significant practical impli-

cations. Firstly, identifying spatial disparities enables policy-makers to

formulate targeted and region-specific carbon emission reduction poli-

cies. Tailored measures can maximize the benefits of carbon reduction

by understanding each region's unique challenges and potential. Recog-

nizing complementarities between regions presents opportunities for

regional cooperation and collaborative initiatives. Synergistic efforts

can be fostered by leveraging strengths, resources, sharing best prac-

tices, technology transfer, and joint research and development projects.

The application of the study's findings extends beyond policy-making

and inter-regional collaboration. Industry practitioners can utilize iden-

tified complementarities to explore partnerships, resource sharing, and

market opportunities, enhancing competitiveness and promoting

greener operations. Overall, this study contributes to sustainable devel-

opment by providing actionable insights into the spatial dynamics of

carbon emission reduction. The practical application in real-life scenar-

ios and industry practices can result in more effective strategies, en-

hanced cooperation, and a tangible impact on reducing carbon emis-

sions and promoting a greener future.

Therefore, it is crucial to develop specific policies and measures to

address regional disparities in carbon emissions. Implementing strin-

gent emission standards and restrictions for regions with high carbon

emissions can compel enterprises and factories to adopt clean energy

sources or improve production processes to minimize emissions. Con-

versely, regions with lower carbon emissions can focus on enhancing

energy efficiency, fostering technological innovation, and using renew-

able energy. Establishing a cross-regional cooperation mechanism en-

ables sharing of experiences, technologies, and resources among differ-

ent regions, fostering collaborative efforts in carbon reduction. High-

carbon emitting regions can collaborate with low-carbon emitting re-

gions, facilitating carbon emission reduction through resource transfer

and technical support. Promoting cross-regional energy interaction and

the development of an electricity network can enhance efficient energy

use and carbon emission reduction. It is essential to encourage the

transformation and upgrading of high-carbon-emitting industries while

promoting the growth of green and low-carbon industries. Policy sup-

port, technological innovation, and market orientation can facilitate

the expansion of clean energy, energy conservation, environmental pro-

tection, and circular economy industries, reducing the environmental

impact of high-carbon emission industries. Moreover, enhancing public

awareness and education and increasing community engagement in

carbon emission reduction is crucial. Through publicity and educa-

tional activities, the public can be guided towards adopting a low-

carbon lifestyle, encouraging energy-saving practices, and promoting

the overall green transformation of society.

It is imperative to consider the following recommendations in addi-

tion to addressing the limitations of the current study to enhance the

quality and impact of future research: Building upon specific research

findings: Subsequent studies should delve deeper into the specific find-

ings of this study and thoroughly explore their implications. It may en-

tail conducting additional analyses, gathering more comprehensive

data, or examining related factors to understand the phenomenon un-

der investigation comprehensively. Testing theories, frameworks, or

models in new contexts, locations, or cultures: Transferring theories,

frameworks, or models to different contexts, locations, or cultures can

yield valuable insights into their applicability and generalizability. Re-

searchers should consider conducting comparative studies or replicat-

ing the research in diverse settings to validate or extend existing theo-

ries. These aspects will help solidify future research, providing valuable

CORRECTED PROOF

K. Di et al. Journal of Cleaner Production xxx (xxxx) 138436

insights to inform practice and policy decisions. By addressing limita-

tions, delving into specific findings, improving research design, testing

in different contexts, and expanding theoretical frameworks, re-

searchers can contribute to advancing knowledge in their respective

fields.

Fund project

This research has received funding from Postgraduate Innovation

Project at Qinghai Minzu University:Study on the practical path of pro-

moting green, low-carbon and circular development in Qinghai

Province (NO: 39D2023004).

Funding for APC:Qinghai Minzu University (Taxpayer Identification

Number:12630000440001728W).

Ethical approval

All applicable international, national, and/or institutional guide-

lines for the care and use of animals were followed.

Author contributions statement

Kaisheng Di: Conceptualization, Methodology, Investiga-

tion, Writing -Original Draft, Writing - Review & Editing, Fund-

ing acquisition; Weidong Chen, Xingnian Zhang: Methodology,

Writing - Original Draft, Investigation, Supervision; Quanling

Cai: Investigation, Supervision; Dongli Li: Investigation, Super-

vision; Caiping Liu: Investigation, Supervision; Qiumei

Shi,Zhensheng Di:Investigation, Supervision.

Uncited references

Herrador et al., 2022b,Pires Da Mata Costa et al., 2021

Declaration of competing interest

The authors declare that they have no known competing financial

interests or personal relationships that could have appeared to influ-

ence the work reported in this paper.

Data availability

Data will be made available on request.

Acknowledgements

This research has received funding from Postgraduate Innovation

Project at Qinghai Minzu University:Study on the practical path of pro-

moting green, low-carbon and circular development in Qinghai

Province(NO:39D2023004).Funding for APC:Qinghai Minzu University

(Taxpayer Identification Number:12630000440001728W).

References

AliAkbari, R., Ghasemi, M.H., Neekzad, N., Kowsari, E., Ramakrishna, S., Mehrali, M.,

Marfavi, Y., 2021. High value add bio-based low-carbon materials: conversion

processes and circular economy. J. Clean. Prod. 293, 126101.

Awan, U., Shamim, S., Khan, Z., Zia, N.U., Shariq, S.M., Khan, M.N., 2021. Big data

analytics capability and decision-making: the role of data-driven insight on circular

economy performance. Technol. Forecast. Soc. Change 168, 120766. https://doi.org/

10.1016/j.techfore.2021.120766.

Balaji, A.B., Rudd, C., Liu, X., 2020. Recycled carbon fibers (rCF) in automobiles: towards

circular economy. Materials Circular Economy 2, 1–8.

Barreiro-Gen, M., Lozano, R., 2020. How circular is the circular economy? Analysing the

implementation of circular economy in organisations. Bus. Strat. Environ. 29 (8),

3484–3494.

Bloise, C., 2020. Collaboration in a circular economy: learning from the farmers to reduce

food waste. J. Enterprise Inf. Manag. 33 (4), 769–789. https://doi.org/10.1108/

JEIM-02-2019-0062.

Bombana, B., Ariza, E., 2019. A double-loop process for beach quality index construction:

approaching the complexity of the Catalan coast. J. Environ. Manag. 240, 177–189.

https://doi.org/10.1016/j.jenvman.2019.03.100.

Bonviu, F., 2014. The European economy: from a linear to a circular economy. Rom. J.

Eur. Aff. 14, 78.

Brändström, J., Saidani, M., 2022. Comparison between circularity metrics and LCA: a

case study on circular economy strategies. J. Clean. Prod. 371, 133537. https://

doi.org/10.1016/j.jclepro.2022.133537.

Budzianowski, W.M., 2017. Implementing carbon capture, utilisation and storage in the

circular economy. Int. J. Glob. Warming 12 (2), 272–296.

Camacho-Otero, J., Boks, C., Pettersen, I.N., 2018. Consumption in the circular economy:

A literature review. Sustainability 10 (8), 2758.

Chen, X., Chen, Y.E., Chang, C.-P., 2019. The effects of environmental regulation and

industrial structure on carbon dioxide emission: a non-linear investigation. Environ.

Sci. Pollut. Control Ser. 26, 30252–30267.

Christensen, T.B., 2021. Towards a circular economy in cities: exploring local modes of

governance in the transition towards a circular economy in construction and textile

recycling. J. Clean. Prod. 305, 127058. https://doi.org/10.1016/

j.jclepro.2021.127058.

Corvellec, H., Stowell, A.F., Johansson, N., 2022. Critiques of the circular economy. J. Ind.

Ecol. 26 (2), 421–432.

De Souza, J.F.T., Pacca, S.A., 2021. Carbon reduction potential and costs through circular

bioeconomy in the Brazilian steel industry. Resour. Conserv. Recycl. 169, 105517.

https://doi.org/10.1016/j.resconrec.2021.105517.

Dewick, P., De Mello, A.M., Sarkis, J., Donkor, F.K., 2022. The puzzle of the informal

economy and the circular economy. Resour. Conserv. Recycl. 187, 106602. https://

doi.org/10.1016/j.resconrec.2022.106602.

Dong, D., Espinoza, L.A.T., Loibl, A., Pfaff, M., Tukker, A., Van Der Voet, E., 2020.

Scenarios for anthropogenic copper demand and supply in China: implications of a

scrap import ban and a circular economy transition. Resour. Conserv. Recycl. 161,

104943. https://doi.org/10.1016/j.resconrec.2020.104943.

Dong, H., Geng, Y., Xi, F., Fujita, T., 2013. Carbon footprint evaluation at industrial park

level: a hybrid life cycle assessment approach. Energy Pol. 57, 298–307.

Dong, L., Liang, H., Zhang, L., Liu, Z., Gao, Z., Hu, M., 2017. Highlighting regional eco-

industrial development: life cycle benefits of an urban industrial symbiosis and

implications in China. Ecol. Model. 361, 164–176.

Elia, V., Gnoni, M.G., Tornese, F., 2017. Measuring circular economy strategies through

index methods: a critical analysis. J. Clean. Prod. 142, 2741–2751.

Elia, V., Gnoni, M.G., Tornese, F., 2020. Evaluating the adoption of circular economy

practices in industrial supply chains: an empirical analysis. J. Clean. Prod. 273,

122966. https://doi.org/10.1016/j.jclepro.2020.122966.

Erythropel, H.C., Zimmerman, J.B., De Winter, T.M., Petitjean, L., Melnikov, F., Lam,

C.H., Lounsbury, A.W., Mellor, K.E., Janković, N.Z., Tu, Q., Pincus, L.N., Falinski,

M.M., Shi, W., Coish, P., Plata, D.L., Anastas, P.T., 2018. The Green ChemisTREE: 20

years after taking root with the 12 principles. Green Chem. 20 (9), 1929–1961.

https://doi.org/10.1039/C8GC00482J.

Fackler, N., Heijstra, B.D., Rasor, B.J., Brown, H., Martin, J., Ni, Z., Shebek, K.M., Rosin,

R.R., Simpson, S.D., Tyo, K.E., 2021. Stepping on the gas to a circular economy:

accelerating development of carbon-negative chemical production from gas

fermentation. Annu. Rev. Chem. Biomol. Eng. 12, 439–470.

Fang, K., Dong, L., Ren, J., Zhang, Q., Han, L., Fu, H., 2017. Carbon footprints of urban

transition: tracking circular economy promotions in Guiyang, China. Ecol. Model.

365, 30–44.

Fuinhas, J.A., Koengkan, M., Leitão, N.C., Nwani, C., Uzuner, G., Dehdar, F., Relva, S.,

Peyerl, D., 2021. Effect of battery electric vehicles on greenhouse gas emissions in 29

European Union countries. Sustainability 13 (24), 13611.

Galatti, L.G., Baruque-Ramos, J., 2022. Circular economy indicators for measuring social

innovation in the Brazilian textile and fashion industry. J. Clean. Prod. 363, 132485.

https://doi.org/10.1016/j.jclepro.2022.132485.

Gallego-Schmid, A., Chen, H.-M., Sharmina, M., Mendoza, J.M.F., 2020. Links between

circular economy and climate change mitigation in the built environment. J. Clean.

Prod. 260, 121115.

Gebhardt, M., Spieske, A., Birkel, H., 2022. The future of the circular economy and its

effect on supply chain dependencies: empirical evidence from a Delphi study.

Transport. Res. E Logist. Transport. Rev. 157, 102570. https://doi.org/10.1016/

j.tre.2021.102570.

Geisendorf, S., Pietrulla, F., 2018. The circular economy and circular economic

concepts—a literature analysis and redefinition. Thunderbird Int. Bus. Rev. 60 (5),

771–782.

Geissdoerfer, M., Savaget, P., Bocken, N.M., Hultink, E.J., 2017. The Circular Economy–A

new sustainability paradigm? J. Clean. Prod. 143, 757–768.

Geng, Y., Sarkis, J., Bleischwitz, R., 2019. How to globalize the circular economy. Nature

565 (7738), 153–155.

Geng, Y., Sarkis, J., Ulgiati, S., Zhang, P., 2013. Measuring China’s circular economy.

Science 339 (6127), 1526–1527.

Gigli, S., Landi, D., Germani, M., 2019. Cost-benefit analysis of a circular economy project:

a study on a recycling system for end-of-life tyres. J. Clean. Prod. 229, 680–694.

Goyal, S., Chauhan, S., Mishra, P., 2021. Circular economy research: a bibliometric

analysis (2000–2019) and future research insights. J. Clean. Prod. 287, 125011.

https://doi.org/10.1016/j.jclepro.2020.125011.

Grafström, J., Aasma, S., 2021. Breaking circular economy barriers. J. Clean. Prod. 292,

126002. https://doi.org/10.1016/j.jclepro.2021.126002.

Hao, H., Qiao, Q., Liu, Z., Zhao, F., 2017. Impact of recycling on energy consumption and

greenhouse gas emissions from electric vehicle production: the China 2025 case.

Resour. Conserv. Recycl. 122, 114–125.

Herrador, M., de Jong, W., Nasu, K., Granrath, L., 2022a. Circular economy and zero-

CORRECTED PROOF

K. Di et al. Journal of Cleaner Production xxx (xxxx) 138436

carbon strategies between Japan and South Korea: a comparative study. Sci. Total

Environ. 820, 153274.

Herrador, M., De Jong, W., Nasu, K., Granrath, L., 2022b. Circular economy and zero-

carbon strategies between Japan and South Korea: a comparative study. Sci. Total

Environ. 820, 153274. https://doi.org/10.1016/j.scitotenv.2022.153274.

Hertwich, E.G., 2021. Increased carbon footprint of materials production driven by rise in

investments. Nat. Geosci. 14 (3), 151–155. https://doi.org/10.1038/s41561-021-

00690-8.

Heshmati, A., 2017. A review of the circular economy and its implementation. Int. J.

Green Econ. 11 (3–4), 251–288.

Keijer, T., Bakker, V., Slootweg, J.C., 2019. Circular chemistry to enable a circular

economy. Nat. Chem. 11 (3), 190–195.

Khan, K., Su, C.W., Khurshid, A., 2022. Circular economy: the silver bullet for emissions?

J. Clean. Prod. 379, 134819. https://doi.org/10.1016/j.jclepro.2022.134819.

Khan, M.A.-A., Cárdenas-Barrón, L.E., Treviño-Garza, G., Céspedes-Mota, A., 2023.

Optimal circular economy index policy in a production system with carbon emissions.

Expert Syst. Appl. 212, 118684.

Khayyam, H., Naebe, M., Milani, A.S., Fakhrhoseini, S.M., Date, A., Shabani, B., Atkiss, S.,

Ramakrishna, S., Fox, B., Jazar, R.N., 2021. Improving energy efficiency of carbon

fiber manufacturing through waste heat recovery: a circular economy approach with

machine learning. Energy 225, 120113.

Kim, I., Jang, Y., 2022. Material efficiency and greenhouse gas reduction effect of

industrial waste by material circulation in Korea. J. Clean. Prod. 376, 134053.

https://doi.org/10.1016/j.jclepro.2022.134053.

Kirchherr, J., Piscicelli, L., Bour, R., Kostense-Smit, E., Muller, J., Huibrechtse-Truijens,

A., Hekkert, M., 2018. Barriers to the circular economy: evidence from the European

union (EU). Ecol. Econ. 150, 264–272.

Kirchherr, J., Reike, D., Hekkert, M., 2017. Conceptualizing the circular economy: an

analysis of 114 definitions. Resour. Conserv. Recycl. 127, 221–232.

Korhonen, J., Honkasalo, A., Seppälä, J., 2018. Circular economy: the concept and its

limitations. Ecol. Econ. 143, 37–46.

Kumar Awasthi, M., Yan, B., Sar, T., Gómez-García, R., Ren, L., Sharma, P., Binod, P.,

Sindhu, R., Kumar, V., Kumar, D., Mohamed, B.A., Zhang, Z., Taherzadeh, M.J., 2022.

Organic waste recycling for carbon smart circular bioeconomy and sustainable

development: a review. Bioresour. Technol. 360, 127620. https://doi.org/10.1016/

j.biortech.2022.127620.

Kurniawan, T.A., Othman, M.H.D., Liang, X., Goh, H.H., Gikas, P., Chong, K.-K., Chew,

K.W., 2023. Challenges and opportunities for biochar to promote circular economy

and carbon neutrality. J. Environ. Manag. 332, 117429.

Lehmann, C., Cruz-Jesus, F., Oliveira, T., Damásio, B., 2022. Leveraging the circular

economy: investment and innovation as drivers. J. Clean. Prod. 360, 132146. https://

doi.org/10.1016/j.jclepro.2022.132146.

Li, W., Bao, L., Wang, L., Li, Y., Mai, X., 2019. Comparative evaluation of global low-

carbon urban transport. Technol. Forecast. Soc. Change 143, 14–26. https://doi.org/

10.1016/j.techfore.2019.02.008.

Liu, X., Xin, L., 2023. Spatial and temporal evolution and greenhouse gas emissions of

China’s agricultural plastic greenhouses. Sci. Total Environ. 863, 160810. https://

doi.org/10.1016/j.scitotenv.2022.160810.

MacArthur, E., 2013. Towards the circular economy. J. Ind. Ecol. 2 (1), 23–44.

Magazzino, C., Falcone, P.M., 2022. Assessing the relationship among waste generation,

wealth, and GHG emissions in Switzerland: some policy proposals for the

optimization of the municipal solid waste in a circular economy perspective. J. Clean.

Prod. 351, 131555.

Mgbechidinma, C.L., Akan, O.D., Zhang, C., Huang, M., Linus, N., Zhu, H., Wakil, S.M.,

2022. Integration of green economy concepts for sustainable biosurfactant production

–a review. Bioresour. Technol. 364, 128021. https://doi.org/10.1016/

j.biortech.2022.128021.

Minunno, R., O’Grady, T., Morrison, G.M., Gruner, R.L., 2020. Exploring environmental

benefits of reuse and recycle practices: a circular economy case study of a modular

building. Resour. Conserv. Recycl. 160, 104855.

Moraga, G., Huysveld, S., Mathieux, F., Blengini, G.A., Alaerts, L., Van Acker, K., De

Meester, S., Dewulf, J., 2019a. Circular economy indicators: what do they measure?

Resour. Conserv. Recycl. 146, 452–461.

Moraga, G., Huysveld, S., Mathieux, F., Blengini, G.A., Alaerts, L., Van Acker, K., De

Meester, S., Dewulf, J., 2019b. Circular economy indicators: what do they measure?

Resour. Conserv. Recycl. 146, 452–461. https://doi.org/10.1016/

j.resconrec.2019.03.045.

Morseletto, P., 2020a. Targets for a circular economy. Resour. Conserv. Recycl. 153,

104553.

Morseletto, P., 2020b. Targets for a circular economy. Resour. Conserv. Recycl. 153,

104553. https://doi.org/10.1016/j.resconrec.2019.104553.

Murray, A., Skene, K., Haynes, K., 2017. The circular economy: an interdisciplinary

exploration of the concept and application in a global context. J. Bus. Ethics 140,

369–380.

Nabernegg, S., Bednar-Friedl, B., Muñoz, P., Titz, M., Vogel, J., 2019. National policies for

global emission reductions: effectiveness of carbon emission reductions in

international supply chains. Ecol. Econ. 158, 146–157.

Negrete-Cardoso, M., Rosano-Ortega, G., Álvarez-Aros, E.L., Tavera-Cortés, M.E., Vega-

Lebrún, C.A., Sánchez-Ruíz, F.J., 2022. Circular economy strategy and waste

management: a bibliometric analysis in its contribution to sustainable development,

toward a post-COVID-19 era. Environ. Sci. Pollut. Control Ser. 29 (41), 61729–61746.

https://doi.org/10.1007/s11356-022-18703-3.

Norman, J., MacLean, H.L., Kennedy, C.A., 2006. Comparing high and low residential

density: life-cycle analysis of energy use and greenhouse gas emissions. J. Urban

Plann. Dev. 132 (1), 10–21.

Ormazabal, M., Prieto-Sandoval, V., Puga-Leal, R., Jaca, C., 2018a. Circular economy in

Spanish SMEs: challenges and opportunities. J. Clean. Prod. 185, 157–167.

Ormazabal, M., Prieto-Sandoval, V., Puga-Leal, R., Jaca, C., 2018b. Circular economy in

Spanish SMEs: challenges and opportunities. J. Clean. Prod. 185, 157–167. https://

doi.org/10.1016/j.jclepro.2018.03.031.

Palmer, M.A., Ambrose, R.F., Poff, N.L., 1997. Ecological theory and community

restoration ecology. Restor. Ecol. 5 (4), 291–300.

Palmer, M.A., Zedler, J.B., Falk, D.A., 2016. Ecological theory and restoration ecology.

Foundations of Restoration Ecology 3–26.

Pavan, M., Reinmets, K., Garg, S., Mueller, A.P., Marcellin, E., Köpke, M., Valgepea, K.,

2022. Advances in systems metabolic engineering of autotrophic carbon oxide-fixing

biocatalysts towards a circular economy. Metab. Eng. 71, 117–141. https://doi.org/

10.1016/j.ymben.2022.01.015.

Peng, B., Sheng, X., Wei, G., 2020. Does environmental protection promote economic

development? From the perspective of coupling coordination between environmental

protection and economic development. Environ. Sci. Pollut. Control Ser. 27 (31),

39135–39148. https://doi.org/10.1007/s11356-020-09871-1.

Pires da Mata Costa, L., Micheline Vaz de Miranda, D., Couto de Oliveira, A.C., Falcon, L.,

Stella Silva Pimenta, M., Guilherme Bessa, I., Juarez Wouters, S., Andrade, M.H.S.,

Pinto, J.C., 2021. Capture and reuse of carbon dioxide (CO2) for a plastics circular

economy: a review. Processes 9 (5), 759.

Pires Da Mata Costa, L., Micheline Vaz De Miranda, D., Couto De Oliveira, A.C., Falcon, L.,

Stella Silva Pimenta, M., Guilherme Bessa, I., Juarez Wouters, S., Andrade, M.H.S.,

Pinto, J.C., 2021. Capture and reuse of carbon dioxide (CO2) for a plastics circular

economy: a review. Processes 9 (5), 759. https://doi.org/10.3390/pr9050759.

Poveda, A.C., Carvajal, J.E.M., Pulido, N.R., 2019. Relations between economic

development, violence and corruption: a nonparametric approach with DEA and data

panel. Heliyon 5 (4), e01496. https://doi.org/10.1016/j.heliyon.2019.e01496.

Rajput, S., Singh, S.P., 2019. Connecting circular economy and industry 4.0. Int. J. Inf.

Manag. 49, 98–113.

Rashid, M.I., Shahzad, K., 2021. Food waste recycling for compost production and its

economic and environmental assessment as circular economy indicators of solid

waste management. J. Clean. Prod. 317, 128467. https://doi.org/10.1016/

j.jclepro.2021.128467.

Rehman Khan, S.A., Zhang, Y., Anees, M., Golpîra, H., Lahmar, A., Qianli, D., 2018. Green

supply chain management, economic growth and environment: a GMM based

evidence. J. Clean. Prod. 185, 588–599. https://doi.org/10.1016/

j.jclepro.2018.02.226.

Saidani, M., Yannou, B., Leroy, Y., Cluzel, F., Kendall, A., 2019. A taxonomy of circular

economy indicators. J. Clean. Prod. 207, 542–559. https://doi.org/10.1016/

j.jclepro.2018.10.014.

Santagata, R., Zucaro, A., Viglia, S., Ripa, M., Tian, X., Ulgiati, S., 2020. Assessing the

sustainability of urban eco-systems through Emergy-based circular economy

indicators. Ecol. Indicat. 109, 105859. https://doi.org/10.1016/

j.ecolind.2019.105859.

Sarkar, B., Debnath, A., Chiu, A.S., Ahmed, W., 2022. Circular economy-driven two-stage

supply chain management for nullifying waste. J. Clean. Prod. 339, 130513.

Sherwood, J., Gongora, G.T., Velenturf, A.P.M., 2022. A circular economy metric to

determine sustainable resource use illustrated with neodymium for wind turbines. J.

Clean. Prod. 376, 134305. https://doi.org/10.1016/j.jclepro.2022.134305.

Smol, M., Koneczna, R., 2021. Economic indicators in water and wastewater sector

contributing to a circular economy (CE). Resources 10 (12), 129. https://doi.org/

10.3390/resources10120129.

Stahel, W.R., 2016. The circular economy. Nature 531 (7595), 435–438.

Stahel, W.R., 2019. The Circular Economy: A User’s Guide. Routledge.

Stegmann, P., Daioglou, V., Londo, M., Van Vuuren, D.P., Junginger, M., 2022. Plastic

futures and their CO2 emissions. Nature 612 (7939), 272–276. https://doi.org/

10.1038/s41586-022-05422-5.

Suárez-Eiroa, B., Fernández, E., Méndez, G., 2021. Integration of the circular economy

paradigm under the just and safe operating space narrative: twelve operational

principles based on circularity, sustainability and resilience. J. Clean. Prod. 322,

129071.

Suchek, N., Fernandes, C.I., Kraus, S., Filser, M., Sjögrén, H., 2021. Innovation and the

circular economy: a systematic literature review. Bus. Strat. Environ. 30 (8),

3686–3702.

Tao, Y., Li, H., Wen, Z., Chen, H., Xu, W., Evans, S., 2019. A hybrid scenario analysis for

the selection of future greenhouse gas emissions reduction technologies in China’s oil

and gas industry. J. Clean. Prod. 223, 14–24. https://doi.org/10.1016/

j.jclepro.2019.03.144.

Tauseef Hassan, S., Wang, P., Khan, I., Zhu, B., 2023. The impact of economic complexity,

technology advancements, and nuclear energy consumption on the ecological

footprint of the USA: towards circular economy initiatives. Gondwana Res. 113,

237–246. https://doi.org/10.1016/j.gr.2022.11.001.

Tcvetkov, P., Cherepovitsyn, A., Fedoseev, S., 2019. The changing role of CO2 in the

transition to a circular economy: review of carbon sequestration projects.

Sustainability 11 (20), 5834.

Van Doren, D., Driessen, P.P., Runhaar, H., Giezen, M., 2018. Scaling-up low-carbon

urban initiatives: towards a better understanding. Urban Stud. 55 (1), 175–194.

https://doi.org/10.1177/0042098016640456.

Van Eemeren, F.H., Grootendorst, R., 2004. A Systematic Theory of Argumentation: the

Pragma-Dialectical Approach. Cambridge University Press.

Van Ewijk, S., Stegemann, J.A., Ekins, P., 2020. Limited climate benefits of global

recycling of pulp and paper. Nat. Sustain. 4 (2), 180–187. https://doi.org/10.1038/

s41893-020-00624-z.

Van Oorschot, J., Sprecher, B., Roelofs, B., Van Der Horst, J., Van Der Voet, E., 2022.

Towards a low-carbon and circular economy: scenarios for metal stocks and flows in

the Dutch electricity system. Resour. Conserv. Recycl. 178, 106105. https://doi.org/

CORRECTED PROOF

K. Di et al. Journal of Cleaner Production xxx (xxxx) 138436

10.1016/j.resconrec.2021.106105.

Vela, I.C., Vilches, T.B., Berndes, G., Johnsson, F., Thunman, H., 2022. Co-recycling of

natural and synthetic carbon materials for a sustainable circular economy. J. Clean.

Prod. 365, 132674.

Velenturf, A.P., Purnell, P., 2021. Principles for a sustainable circular economy. Sustain.

Prod. Consum. 27, 1437–1457.

Vinante, C., Sacco, P., Orzes, G., Borgianni, Y., 2021. Circular economy metrics: literature

review and company-level classification framework. J. Clean. Prod. 288, 125090.

https://doi.org/10.1016/j.jclepro.2020.125090.

Vujanović, A., Puhar, J., Čolnik, M., Plohl, O., Vidovič, T., Volmajer Valh, J., Škerget, M.,

Čuček, L., 2022. Sustainable industrial ecology and environmental analysis: a case of

melamine etherified resin fibres. J. Clean. Prod. 369, 133301. https://doi.org/

10.1016/j.jclepro.2022.133301.

Wang, H., Schandl, H., Wang, X., Ma, F., Yue, Q., Wang, G., Wang, Y., Wei, Y., Zhang, Z.,

Zheng, R., 2020. Measuring progress of China’s circular economy. Resour. Conserv.

Recycl. 163, 105070.

Wang, N., Guo, Z., Meng, F., Wang, H., Yin, J., Liu, Y., 2019. The circular economy and

carbon footprint: a systematic accounting for typical coal-fuelled power industrial

parks. J. Clean. Prod. 229, 1262–1273.

Wei, Y., Huang, C., Lam, P.T., Yuan, Z., 2015. Sustainable urban development: a review on

urban carrying capacity assessment. Habitat Int. 46, 64–71.

Wich, T., Lueke, W., Deerberg, G., Oles, M., 2020. Carbon2Chem®-CCU as a step toward a

circular economy. Front. Energy Res. 7, 162.

Wijkman, A., Skånberg, K., 2015. The Circular Economy and Benefits for Society. Club of

Rome.

Winans, K., Kendall, A., Deng, H., 2017. The history and current applications of the

circular economy concept. Renew. Sustain. Energy Rev. 68, 825–833.

Xue, Y., Luan, W., Wang, H., Yang, Y., 2019. Environmental and economic benefits of

carbon emission reduction in animal husbandry via the circular economy: case study

of pig farming in Liaoning, China. J. Clean. Prod. 238, 117968. https://doi.org/

10.1016/j.jclepro.2019.117968.

Yong, R., 2007. The circular economy in China. J. Mater. Cycles Waste Manag. 9,

121–129.

Yrjälä, K., Ramakrishnan, M., Salo, E., 2022. Agricultural Waste Streams as Resource in

Circular Economy for Biochar Production towards Carbon Neutrality. Current

Opinion in Environmental Science & Health, 100339.

Yuan, Z., Bi, J., Moriguichi, Y., 2006. The circular economy: a new development strategy

in China. J. Ind. Ecol. 10 (1–2), 4–8.

Yunan, X., Weixin, L., Yujie, Y., Hui, W., 2021. Evolutionary game for the stakeholders in

livestock pollution control based on circular economy. J. Clean. Prod. 282, 125403.

Zhang, J., Zhang, Y., 2020. Assessing the low-carbon tourism in the tourism-based urban

destinations. J. Clean. Prod. 276, 124303. https://doi.org/10.1016/

j.jclepro.2020.124303.

Zhang, X., Liu, Y., 2022. Circular economy is game-changing municipal wastewater

treatment technology towards energy and carbon neutrality. Chem. Eng. J. 429,

132114. https://doi.org/10.1016/j.cej.2021.132114.

Zhou, Y., Liu, W., Lv, X., Chen, X., Shen, M., 2019. Investigating interior driving factors

and cross-industrial linkages of carbon emission efficiency in China’s construction

industry: based on Super-SBM DEA and GVAR model. J. Clean. Prod. 241, 118322.

https://doi.org/10.1016/j.jclepro.2019.118322.

Zink, T., Geyer, R., 2017. Circular economy rebound. J. Ind. Ecol. 21 (3), 593–602.

The author Kaisheng Di:PhD, Tianjin University. Secretary of the Youth League Commit-

tee of the Party School of Qinghai Province. Member of Qinghai Provincial Organ Youth

League Working Committee. Contributing editor of the Journal of Information Systems

Education. The reviewer for the Annals of Operations Research and the Journal of Current

Scientific Research.

The author Weidong Chen: Professor of Excellence, Doctoral Supervisor, Department of

Management and Economics, Tianjin University, Excellent Talent of the New (Trans) Cen-

tury of the Ministry of Education, Chief Expert of the Major Projects of the National Social

Science Foundation, Standing Director of the Energy, Resources and Environment Com-

mittee of the Chinese Society of Systems Engineering, Tianjin Outstanding Scientific and

Technological Worker, and Invited Expert of Tianjin Xi Jinping Socialist Ideology of So-

cialist Ideology with Chinese Characteristics in the New Era Lecture Troupe. His research

interests include complex system modelling and optimization, energy and resource envi-

ronment policy, low carbon economy mechanism design, regional strategic planning and

organisational performance management. As the project leader, he has presided over one

major project of the National Social Science Foundation of China, three projects of the Na-

tional Natural Science Foundation of China (evaluated as “excellent”after the project),

and more than 40 other national, provincial and ministerial projects. He has been pub-

lished in European Journal of Operational Research, Omega-International Journal of Man-

agement Science, International Journal of Production Research, Energy Economics, Inter-

national Journal of Production Research, and International Journal of Production Re-

search. He has published more than 150 papers in high-level academic journals, such as

Energy Economics, Energy, Applied Energy, Energy Policy, Journal of Physics, Theory and

Practice of Systems Engineering, Journal of Systems Engineering, Chinese Administration,

China Population, Resources and Environment, etc., and his research results have been

cited more than 1200 times, and the number of H-values of his research results has been

increased by more than 10,000 times. The number of citations of research results exceeds

1200, H-index reaches 14, and 6 patents have been applied for. One of the representative

results is one of the top 1% of highly cited papers in ESI, and won the award for the most

highly cited article in the journal “Resources, Conservation & Recycling”. The research re-

sults were awarded the second prize (the first one) of the Eighth Award for Outstanding

Achievements in Scientific Research in Colleges and Universities (Humanities and Social

Sciences), and two first prizes, 4 s prizes, and one third prize of scientific research

achievements at the provincial and ministerial levels or above.

The author Xingnian Zhang: Professor, Doctoral Supervisor, is currently the Dean of the

School of Politics and Public Administration. Qinghai Minzu University.

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Dec 2022
NATURE

Plastics show the strongest production growth of all bulk materials and are already responsible for 4.5% of global greenhouse gas emissions1,2. If no new policies are implemented, we project a doubling of global plastic demand by 2050 and more than a tripling by 2100, with an almost equivalent increase in CO2 emissions. Here we analyse three alternative CO2 emission-mitigation pathways for the global plastics sector until 2100, covering the entire life cycle from production to waste management. Our results show that, through bio-based carbon sequestration in plastic products, a combination of biomass use and landfilling can achieve negative emissions in the long term; however, this involves continued reliance on primary feedstock. A circular economy approach without an additional bioeconomy push reduces resource consumption by 30% and achieves 10% greater emission reductions before 2050 while reducing the potential of negative emissions in the long term. A circular bioeconomy approach combining recycling with higher biomass use could ultimately turn the sector into a net carbon sink, while at the same time phasing out landfilling and reducing resource consumption. Our work improves the representation of material flows and the circular economy in global energy and emission models, and provides insight into long-term dynamics in the plastics sector.

A circular economy metric to determine sustainable resource use illustrated with neodymium for wind turbines

Article

Full-text available

Sep 2022
J CLEAN PROD

The finite capacity of the Earth to provide the resources needed to make products is beginning to dictate policy decisions and citizen behaviours. Herein a methodology is proposed that considers the function (i.e., efficiency and durability) of a product as a way of normalising and hence justifying its resource use. Titled ‘Performance-weighted abiotic Resource Depletion’ (PwRD), this approach allows the resource use of different products to be directly compared, analogous to an absolute sustainability assessment. The PwRD metric quantifies concerns over the supply risk of elements and indicates reasonable actions to sustain a circular economy. This new format of circularity indicator is explained with the case study of neodymium for wind turbine magnets. Individual products as well as larger infrastructure projects such as wind farms can be assessed. It was found that the electrical energy produced by a wind turbine in the USA does not justify the quantity of neodymium required. Demand for the function of products is a variable in PwRD and is equally important as resource use in sustaining a circular economy. In regions of low electricity demand per capita such as the Philippines and Pakistan, the same quantity of neodymium as used in a wind turbine installed in the USA was found to be acceptable for sustaining a circular economy.

Comparison between circularity metrics and LCA: A case study on circular economy strategies

Article

Full-text available

Aug 2022
J CLEAN PROD

The concept of circular economy consists of a wide range of strategies that aim to reduce the environmental impact of production systems and consumption patterns through increased circularity of resources. Circularity is mostly associated with material efficiency strategies that either close, slow or narrow loops, and a multitude of circularity metrics have been developed to evaluate the efficiency of such strategies. Relatively little effort has been made to quantitatively assess the connection between circularity, material efficiency, and environmental sustainability. More knowledge is required about how material-based circularity metrics can be used to guide practitioners of the circular economy towards strategies that foster environmental sustainability. In this study, a novel structured approach is adopted to perform such a comparison by including closing, slowing, and narrowing strategies on a lawn mowing case. Four circularity metrics that can capture material strategies throughout product value chains (Material Efficiency Metric, Material Circularity Indicator, Circularity Potential Indicator and Circular Economy Indicator Prototype) are compared to three complementary midpoint categories using Life Cycle Assessment (Global Warming Potential, Material Resource Scarcity, and Human Non-Carcinogenic Toxicity). The results show that the studied circularity metrics generate accurate results when evaluating Material Resource Scarcity and that they rank the material efficiency strategies equally with all environmental impact categories. The circularity metrics are unable to capture the benefits of reduced energy and the correspondence to all impact categories is lower in scenarios with higher energy use. We conclude that the strength of the studied circularity metrics is twofold: i) promoting solutions that reduce material demand and waste creation and ii) highlighting the advantages of combining complementary circularity strategies. This research shows that the material-based circularity metrics can be valuable guidance tools for practitioners of circular economy, as they do not require methodological expertise and can align the results with Life Cycle Assessments in some specific situations. More comparisons between circularity metric results and Life Cycle Assessments are needed in future research to establish state-of-the-art circularity metrics for specific situations and purposes, including energy-focused circularity metrics.

Challenges and opportunities for biochar to promote circular economy and carbon neutrality

Article

Feb 2023
J ENVIRON MANAGE

Ki Tí

Biochar, derived from unused biomass, is widely considered for its potential to deal with climate change problems. Global interest in biochar is attributed to its ability to sequester carbon in soil and to remediate aquatic environment from water pollution. As soil conditioner and/or adsorbent, biochar offers opportunity through a circular economy (CE) paradigm. While energy transition continues, progress toward low-emissions materials accelerates their advance towards net-zero emissions. However, none of existing works addresses CE-based biochar management to achieve carbon neutrality. To reflect its novelty, this work provides a critical overview of challenges and opportunities for biochar to promote CE and carbon neutrality. This article also offers seminal perspectives about strengthening biomass management through CE and resource recovery paradigms, while exploring how the unused biomass can promote net zero emissions in its applications. By consolidating scattered knowledge in the body of literature into one place, this work uncovers new research directions to close the loops by implementing the circularity of biomass resources in various fields. It is conclusive from a literature survey of 113 articles (2003-2023) that biomass conversion into biochar can promote net zero emissions and CE in the framework of the UN Sustainable Development Goals (SDGs). Depending on their physico-chemical properties, biochar can become a suitable feedstock for CE. Biochar application as soil enrichment offsets 12% of CO 2 emissions by land use annually. Adding biochar to soil can improve its health and agricultural productivity , while minimizing about 1/8 of CO 2 emissions. Biochar can also sequester CO 2 in the long-term and prevent the release of carbon back into the atmosphere after its decomposition. This practice could sequester 2.5 gigatons (Gt) of CO 2 annually. With the global biochar market reaching USD 368.85 million by 2028, this work facilitates biochar with its versatile characteristics to promote carbon neutrality and CE applications.

Spatial and temporal evolution and greenhouse gas emissions of China's agricultural plastic greenhouses

Article

Dec 2022
SCI TOTAL ENVIRON

The rapid development of the use of agricultural plastic greenhouses in China in recent years has played an important role in the generation of economic benefits from agriculture. However, it has also led to severe negative environmental impacts. Thus, it is necessary to explore the spatial and temporal evolution of agricultural plastic greenhouses in China as well as their environmental impacts so as to obtain a deeper understanding of agricultural intensification and the sustainable development of the facility agriculture industry. To this end, this study analyzed the spatial and temporal evolution characteristics of agricultural plastic greenhouses and their greenhouse gas emissions within the 2000–2020 period based on the extraction of the spatial distribution of greenhouses in China in 2000, 2010, and 2020. Landsat imagery and statistical data combined with the random forest classification method as well as life cycle analysis, were used in this study. The results obtained showed that: (1) Within the 2000–2020 period, the area covered by agricultural plastic greenhouses in China expanded from 1594.66 to 11,838.77 km², with most of the greenhouses predominantly distributed in the North China Plain. (2) The increase in total greenhouse gas emissions between 2000 and 2010 was mainly due to the expansion of agricultural plastic greenhouse use and an increase in agricultural material inputs, whereas the increase between 2010 and 2020 was mainly due to the expansion of agricultural plastic greenhouse use. (3) Greenhouse gas emissions from nitrogenous fertilizers and greenhouse construction materials accounted for 45 % and 36 %, respectively, of the total greenhouse gas emissions observed within the study period. These findings are of great significance as they enhance understanding regarding land intensification development and its accompanying environmental impacts in China.

The impact of economic complexity, technology advancements, and nuclear energy consumption on the ecological footprint of the USA: Towards circular economy initiatives

Article

Nov 2022
GONDWANA RES

The circular economy decouples economic activity from finite resource consumption, creating a resilient system that can tackle global challenges such as climate change, biodiversity loss, waste, and pollution. Nuclear energy has been designated as one of the primary concerns of energy sector modernization because it allows for significant reductions in dangerous material emissions into the environment. Therefore, nuclear energy and improved technologies may become critical growth areas aligned with circular economy principles. We use Dynamic Autoregressive Distributive Lag (DARDL) and Kernel-based Regularized Least Squares (KRLS) to analyze United States data from 1985 to 2016 empirically. The DARDL result shows a positive relationship between ecological footprint and economic complexity, increasing short-term environmental costs. However, nuclear power generation and improved technology significantly reduce ecological concerns. Economic complexity is explored in this work in more nuanced terms, emphasizing the importance of considering the external environment when implementing different economic activities. Policy implications, study limitations, and future research directions are discussed.

Circular economy: The silver bullet for emissions?

Article

Oct 2022
J CLEAN PROD

Sustainable economic development with minimal negative environmental impact is one of the biggest global challenges. The linear system concludes because of rising consumption, production, and economic growth, which makes the system out of resources and results in economic crisis and environmental pollution. The rolling window approach used in the study provides detailed information regarding the relationship between carbon emissions and the circular economy in the Netherlands from 2000:01 to 2020:12. In the full sample causality, the finding reveals the positive influence of the circular economy on carbon emissions. The rolling window explores the causal impact in the sub-sample when there is a time-varying effect. It finds that the circular economy has a negative influence on carbon emissions in certain sub-samples due to a variety of causes. On the other hand, carbon emissions have improved the circular economy's ability to address environmental challenges. Thus, our findings prove the existence of a reciprocal link between the circular economy and carbon emissions. Transitioning to the circular economy setup requires the comprehensive development of models for controlling emissions. The detailed understanding derived from the circular economy will help policymakers to plan the policy for mitigating environmental issues.

Integration of Green Economy Concepts for Sustainable Biosurfactant Production - A Review

Article

Nov 2022
BIORESOURCE TECHNOL

The link between increasing global population, food demand, industrialization, and agricultural waste is strong. Decomposing by-products from food cycles can introduce harmful toxic heavy metals, active degrading microbes, and enzymes to the environment. Additionally, high greenhouse gas emissions from the decomposing wastes contribute to global change and a high carbon economy. The bioeconomy and circular economy of biosurfactant production utilize these cheap feedstocks and promote waste to valuable product initiatives. Waste reduction, reuse, and recycling in an integrating green economy bioprocess ensure the sustainability of novel, cost-effective, safe, and renewable health-grade biosurfactants. This work reviews green economy concepts integration with sustainable biosurfactant production and its application in health-related industries. Benefits from recent advances in the production, characterization, and health-wise classification of biosurfactants were further discussed, including its limitations, techno-economic assessment, market evaluations, possible roadblocks, and future directions.

Material efficiency and greenhouse gas reduction effect of industrial waste by material circulation in Korea

Article

Sep 2022
J CLEAN PROD

As a response to climate change, all industries are making efforts to reduce greenhouse gas (GHG) emissions for carbon neutrality in our society. The waste recycling sector is making efforts in reducing GHG emissions in connection with the circular economy. The Korean government enacted the Framework Act on Resource Circulation and is making efforts to convert waste into renewable materials. This study proposed a method for systematic evaluation and management of material efficiency measurement and GHG emissions for analysis of the effects of recycling of industrial wastes. It focuses on recycled materials rather than recycling waste by applying the methods of material flow analysis and life cycle assessment. As a target for the feasibility analysis of the proposed method, slag, which has the largest amount of generated industrial waste, was selected. The GHG emissions in converting the generated slag into cement ingredient, recycled aggregates, aluminum ingot, zinc ingot, steel ingredient, and silicate fertilizer were calculated. Additionally, the GHG reduction effect by converting the sources of GHG emission throughout the entire life cycle of recycled materials into a renewable energy source was calculated. Actually, 1 ton of six recycled materials generated 30.1 kgCO2eq., 23.21 kgCO2eq., 351.0 kgCO2eq., 337.0 kgCO2eq., 27.6 kgCO2eq., and 30.1 kgCO2eq., respectively. Also, six recycled materials were analyzed to potentially reduce GHG emissions of 81.8%, 75.6%, 87.1%, 88.6%, 77.9%, 81.8%, respectively.

Optimal circular economy index policy in a production system with carbon emissions

Article

Aug 2022
EXPERT SYST APPL

To proper use of the limited natural resources, circular economy is an efficacious and alternative approach to many production systems aimed at protecting the natural environment. This work proposes a profit maximizing production system where all the products are produced with a variable circularity level. As the consumers are increasingly social responsible about their consumption practices at present, both the clients’ demand and per unit gross profit are directly influenced by the products’ circularity level and these consequences are investigated by several linear and nonlinear forms. Moreover, the manufacturer’s several activities in the production system like setup operations, warehouse operations and production mechanism are sources of carbon emissions. The utmost aim of this work is to achieve the manufacturer’s joint optimal circularity and production levels with the ambition of environment protection by reducing carbon emissions with the help of proper operational amendments. The global maximum profit for the production manager is determined by exploiting the optimal policies analytically. Nine examples are solved not only to explore the applicability of the derived analytical results but also to investigate how the optimal policies under all considered cases help the manufacturer to increase profit and reduce the carbon emissions significantly. Finally, an analysis is conducted to provide guidelines for the industry manager examining the parameters’ behavioral patterns on the optimal strategies by altering values of the system parameters. This study helps electrical equipment manufacturers especially (for example, mobile manufacturers) to obtain the optimum circular index for the produced items so that they can earn maximum profits and also protect the environment.

Regional unevenness and synergy of carbon emission reduction in China's green low-carbon circular economy

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