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Continuous allocation of carbon emission quota considering different paths to carbon peak: Based on multi-objective optimization
Abstract
The setting of China’s peaking target by 2030 for total CO2 emissions places stricter requirements on low-carbon
production in each province. The allowable CO2 emissions will become a limited resource in the future which
need to be allocated properly. In this paper, a multi-objective optimization method is used to realize the
continuous allocation of carbon emission quota (CEQ) of each province from 2020 to 2030 under two different
peaking paths by comprehensively considering three objectives of cost, efficiency, and fairness. In addition, the
30 provinces are divided into 4 groups and the distribution results were compared within and between groups.
The results show that: (1) provinces with larger historical carbon emissions will be allocated more CEQ, while
provinces with smaller carbon emissions will achieve peak carbon earlier, and the key to controlling total carbon
emissions lies in the five provinces (Guangdong, Hebei, Henan, Jiangsu, Shandong) in Group 2; (2) the peak
scenario and CEQ allocation scheme corresponding to path 2 will be more consistent with the future development
requirements of China’s energy, economy and environment; (3) Multi-objective is necessary and the continuous
CEQ allocation scheme will be significant for guiding provinces to arrange future annual production plan.
... Inspired by China's "3060" dual carbon goals, we believe that it requires great enthusiasm to rejuvenate and extend EKC investigations. As Xia et al. (2023) and Li et al. (2023a) argued, it is complex whether regions that can achieve carbon peak through emission transfer should feel relieved that they have escaped the responsibility of carbon emission reduction. For example, some studies suggest that Shenzhen's carbon peak goal can be accomplished ahead of schedule; however, it is not optimistic whether Guangdong Province (where Shenzhen is located) can achieve its overall carbon peak by 2030 (Wang et al. 2019;Xu et al. 2021). ...
Achieving the national emission reduction targets requires joint efforts of all jurisdictions, whose sustainable development is affected by complex economic and environmental interactions among regions. An interregional environmental assessment (IREA) framework is constructed for China using multiregional input–output techniques to unravel the carbon emission connections behind interregional economic activities. Then, consumption-based emission accounting is applied in sustainability assessment, in comparison with production-based environmental Kuznets curve (EKC) tests to examine the role of regional connections in shaping EKC. Empirical results expose significant asymmetric CO2 transfer among regions in China, where the Central and Western regions have become CO2 haven for the Eastern region. EKC is valid at the national level and manifests marked regional differences between production- and consumption-based curves. The pollution haven effect alters the EKC curve by expediting the emission peak in the developed Eastern region while delaying it in the developing Western region. Thus, revisiting EKCs in the IREA framework reveals that ignoring interregional connections would lead to misleading results. Only when both production- and consumption-based EKCs transcend their turning points can we claim that environmental governance has ushered in a new era of sustainable development.
... where TO_AR i represents the theoretical output of the i-th agricultural residue, and OC i and RPR i represent the annual carbon-intensive industries [30]. In response for the rapid growth of CO 2 emission, China pledged to peak CO 2 emissions by 2030 and achieved carbon neutrality by 2060 in 2020, and the targets of carbon peak and carbon neutrality have been added to China's overall plan for ecological conservation [31]. When discussing CO 2 emissions, the following factor should be taken into consideration: carbon intensity of energy, which is defined by CO 2 emissions per unit of energy consumption and is related to the quality and efficiency of energy [33]. ...
Agricultural residues are readily available and inexpensive renewable resources in China and can be utilized for reducing China’s reliance on fossil fuels and CO2 emissions. This study conducted a systematic study of energy production and consumption, CO2 emissions, and agricultural residues available for energy and environmental benefits in China. The gap between the domestic production and consumption of energy in China was large. China’s energy consumption structure was dominated by coal over the past thirty years, which was strongly linked the increasing CO2 emissions in China. China has abundant agricultural residues resource, most of which were utilized as soil amendments, animal feeds, substrates in mushroom cultivation, and industrial materials. The usable agricultural residues for energy were estimated to be about 226.9 million tons in 2020. If those usable agricultural residues would be converted into bio-oil and biochar through pyrolysis, it could not only replace about 102 million tons of crude oil, but also sequestrate about 209 million tons of CO2-equivalent. It is expected that the results from this study can promote the application of agricultural residues in potential of energy production and carbon sequestration.
... [29]. In response for the rapid growth of CO 2 emission, China pledged to peak CO 2 emissions by 2030 and achieved carbon neutrality by 2060 in 2020, and the targets of carbon peak and carbon neutrality have been added to China's overall plan for ecological conservation [30]. ...
China is encountering two big challenges: supply shortage of energy especially liquid fuels and serious environmental problems especially increasing CO 2 emissions. Agricultural residues are readily available and inexpensive renewable resources in China and it can be expected to reduce China’s reliance on fossil fuels and CO 2 emissions through the use of agricultural residues. This study conducted a systematic study of energy production and consumption, CO 2 emissions, and agricultural residues available for energy and environmental benefits. The gap between the domestic production and consumption of energy in China became larger and larger, and China’s energy consumption structure was dominated by coal over the past thirty years. The increasing energy consumption especially coal consumption were strongly linked the increasing CO 2 emissions in China. As the largest agricultural country in the world, China has abundant agricultural residues, most of which were utilized as soil amendments, animal feeds, substrates in mushroom cultivation, and industrial materials. The usable agricultural residues for energy were estimated to be about 226.9 million tons in 2020. If those usable agricultural residues would be converted into bio-oil and biochar through pyrolysis, it could not only replace about 102 million tons of crude oil, but also sequestrate about 209 million tons of CO 2 -equivalent. It is expected that the results from this study can promote the application of agricultural residues in potential of energy production and carbon sequestration.
This study presents a comprehensive analysis of vehicle ownership, energy consumption, and carbon emissions in Guangdong Province, China, from 2020 to 2035 under different scenarios. Key findings highlight the province’s pursuit of carbon peak goals and provide valuable insights into strategies to achieve them. Vehicle ownership in Guangdong is projected to exceed 48 million by 2035, which represents a doubling from 2020. Under both scenarios, internal combustion engine vehicle ownership will peak around 2030 and then gradually decline, while under the enhanced scenario, electric vehicle ownership will exceed 40% by 2035. Enhanced vehicle energy efficiency and reduced annual mileage will lead to a 17% reduction in gasoline and diesel consumption by 2035 in both scenarios. At the same time, there will be a substantial five- to six-fold increase in electricity consumption for vehicles compared to 2020. Both scenarios peak in carbon emissions before 2030, with the enhanced scenario achieving this peak a year earlier. The enhanced scenario outperforms the baseline, reducing carbon emissions by about 21.2% from the peak and 8% relative to 2020. Pure electric vehicles exhibit a significant advantage in reducing carbon emissions per vehicle compared to their internal combustion engine counterparts. Encouraging new energy vehicles, especially pure electric ones, accelerates the carbon emissions peak and lowers overall peak emissions. Accelerating the adoption of electric vehicles, reducing per-vehicle fuel consumption and annual average mileage, and optimizing transportation modes are crucial for carbon peaking from the vehicle fuel cycle. Policy recommendations focus on promoting new energy vehicles, optimizing transportation, and advancing research and technology.
This study preprocessed 2000-2019 energy consumption data for 46 key Sichuan industries using matrix normalization. DBSCAN clustering identified 16 feature classes to objectively group industries. Penalized regression models were then applied for their advantages in overfitting control, high-dimensional data processing, and feature selection - well-suited for the complex energy data. Results showed the second cluster around coal had highest emissions due to production needs. Emissions from gasoline-focused and coke-focused clusters were also significant. Based on this, emission reduction suggestions included clean coal technologies, transportation management, coal-electricity replacement in steel, and industry standardization. The research introduced unsupervised learning to objectively select factors and aimed to explore new emission reduction avenues. In summary, the study identified industry groupings, assessed emissions drivers, and proposed scientific reduction strategies to better inform decision-making using algorithms like DBSCAN and penalized regression models.
Under the ‘dual carbon’ goal, the key to environmental-friendly economic development is to increase total factor productivity (TFP) in a greener way. However, as a key market-based emission reduction mechanism, the exploration related to whether the emission trading system (ETS) promotes green TFP (GTFP) is not deep enough, especially for secondary decomposition of this technical index and for specific industrial subsectors. In this study, combining biennial Malmquist-Luenberger productivity index with multi-period difference in differences model, we assess the technological progress of China’s pilot ETS dynamically. The results show that the pilot ETS promoted GTFP significantly in the industrial subsectors, by 8.5%. The technology change index increased by 17.5%, which is the key action path to increasing GTFP. Furtherly, the mechanism test confirms that the policy works mainly through innovation channel. This study implied that the governance toward carbon-zero economies could be accelerated much more effectively by technological innovation of green.
Owing to the surge in greenhouse gas emissions, climate change is attracting increasing attention worldwide. As the world's largest carbon emitter, the achievement of emission peak and carbon neutrality by China is seen as a milestone in the global response to the threat. By setting different "emission peak" and "carbon neutrality" paths, this study compares the different pathways taken by China towards regional emission reduction to illustrate China's possible contribution to global emission reduction, and analyzes the role that China's economy, population, and technology need to play in this process through the Stochastic Impacts by Regression on Population, Affluence, and Technology model. In terms of path setting, based on actual carbon emissions in various regions from 2000 to 2019 and grid data on land use from 2000 to 2020, the model simulates three emission peak paths to 2030 and two carbon neutrality paths to 2060, thus setting six possible carbon emission trends from 2000 to 2060 in different regions. It is found that the higher the unity of policy objectives at the emission peak stage, the lower the heterogeneity of the inter-regional carbon emission trends. In the carbon neutrality stage, the carbon emissions in the unconstrained symmetrical extension decline state scenario causes the greatest environmental harm. Certain regions must shoulder heavier responsibilities in the realization of carbon neutrality. The economic development level can lead to a rise in carbon emissions at the emission peak stage and inhibit it at the carbon neutrality stage. Furthermore, the dual effects of population scale and its quality level will increase carbon emissions at the emission peak stage and decrease it at the carbon neutrality stage. There will be a time lag between the output of science and technology innovation and its industrialization, while green innovation is a key factor in carbon neutrality. Based on the results, this study puts forward policy suggestions from a macro perspective to better realize China's carbon emission goals.
Emissions reduction and increasing environmental quality have become a global concern to ensure sustainable development and mitigate the negative impacts of global warming and climate change. However, there is a scarcity of research exploring the potential of emission reduction components by using econometric approaches. Thus, the present study investigated the role of economic growth, fossil fuel energy use, renewable energy use, technological innovation, agricultural productivity, and forested area to achieve environmental sustainability by reducing carbon dioxide emissions in Indonesia. Time series data from 1990 to 2020 were utilized by applying the Dynamic Ordinary Least Squares method. The empirical findings revealed that a 1% increase in economic growth and fossil fuel energy use will increase carbon dioxide emissions by 0.36% and 0.67% in Indonesia. Conversely, a 1% increase in renewable energy use, technological innovation, agricultural productivity, and the forested area may lead to carbon dioxide emissions reduction by 0.11%, 0.07%, 0.24%, and 2.87%, respectively in the long run. This article put forward policy recommendations in the areas of low-carbon economy, promoting renewable energy use, financing technological innovation, climate-smart agriculture, and sustainable forest management which would ensure environmental sustainability by reducing emissions in Indonesia.
Modeling equity in the allocation of scarce resources is a fast-growing concern in the humanitarian logistics field. The Gini coefficient is one of the most widely recognized measures of inequity and it was originally characterized by means of the Lorenz curve, which is a mathematical function that links the cumulative share of income to rank-ordered groups in a population. So far, humanitarian logistics models that have approached equity using the Gini coefficient do not actually optimize its original formulation, but they use alternative definitions that do not necessarily replicate that original Gini measure. In this paper, we derive the original Gini coefficient via the Lorenz curve to optimize the effectiveness-equity trade-off in a humanitarian location-allocation problem. We also propose new valid inequalities based on a bounding Lorenz curve to tighten the linear relaxation of our model and develop a clustering-based construction of the Lorenz curve that requires fewer additional constraints and variables than the original one. The computational study, based on the floods and landslides in Rio de Janeiro state, Brazil, reveals that while alternative Gini definitions have interesting properties, they can generate vastly different decisions compared to the original Gini coefficient. In addition, viewed from the perspective of the original Gini coefficient, these decisions can be significantly less equitable.
With the statement of Chinese government on energy saving in 2020 at the United Nations General Assembly, carbon neutral was widely spread as a new concept. As a big country, China has the responsibility and obligation to make its own contribution to global climate change. This paper aims to explore and find effective ways for China to achieve carbon neutrality by 2060. We identify the main factors affecting carbon emissions by STIRPAT model; combined with the scenario analysis, we divide the year 2020 to 2060 into three stages: year 2020-2030 is carbon peak stage, year 2030-2050 is rapid emission reduction stage, and year 2050-2060 is complete carbon neutralization stage. At each stage, three development models, high, medium, and low level, were established. There are a total of 27 different scenarios in three stages. A system dynamics model was established to simulate the effects of carbon emission factors and changes in carbon sinks in different scenarios. Finally, 8 paths were found which in line with Chinese current goal of achieving carbon neutrality with treating reach carbon peak in 2030 as an additional filter condition. Comparing per capita GDP levels in different scenarios, we eventually find that keeping economic development at a low level in the first stage, a high level in the second stage, and a medium level in the finally stage, the point where net carbon emissions are less than zero for the first time will appear between year 2056 and 2057. By then, the per capita GDP will reach 144,500 yuan (based on year 2000), nearly four times 2000's. In all, these findings are helpful for policymakers to implement reasonable policies to achieve carbon emission peaking and carbon neutral in China.
Energy and emission data are crucial to climate change research and mitigation efforts. The accuracy of energy statistics is essential for mitigation strategies and evaluating the performance of low carbon energy transition efforts. This study provides the most up‐to‐date emission inventories for China and its provinces for 2018 and 2019. We also update the carbon dioxide (CO2) emission inventories of China and 30 provinces since 2012 based on the newly revised energy statistics. The inventories are compiled in a combined accounting approach of scope 1 (Intergovernmental Panel on Climate Change territorial emissions from 17 types of fossil fuel combustion and cement production by 47 socioeconomic sectors) and scope 2 (emissions from purchased electricity and heat consumption). The most recent energy revision led to an increase in reported national CO2 emissions by an average of 0.3% from 2014 to 2017. The results show that data revisions raised China's carbon intensity mitigation baseline (in 2005) by 5.1%–10.8% and thus made it more challenging to fulfill the mitigation pledges. However, the 2020 carbon intensity mitigation target was achieved ahead of schedule in 2018. A preliminary estimate of China's national emissions for 2020 shows that the COVID‐19 pandemic and lockdown was not able to offset China's annual increase in CO2 emissions. These emissions inventories provide an improved evidence base for China's policies toward net‐zero emissions.
Regional carbon emission paths have an important impact on the realization of China’s carbon emission peak target. Due to the uncertainty of future development model, the change of carbon emissions will also face uncertainty, which will make achieving the peak target challenging. Taking Shandong, Henan, and Guangdong, three of China’s most populous provinces, as examples, this study analyzed the impacts of uncertainties in carbon accounting principles, driving factors, and simulation mechanism on achieving the peak target. The results show that (1) under the baseline scenario, the accounting principles based on primary energy consumption and IPCC sector consumption will make the peaking time of Guangdong be evaluated as 2018 and 2030, respectively, and the simulation based on IPCC sector accounting will advance the peaking time of Shandong by at least 5 years, while Henan will be less affected. (2) When considering the impact of the energy structure, Guangdong and Henan are estimated to peak in 2011 and 2018, while without considering the impact of the energy structure, the peak in the two provinces may be after 2035. Energy structure has no effect on the estimation of peaking time for Shandong. In addition, the k value in the ridge regression method also has no effect on the peaking time for the three provinces; it only affects the simulations of annual carbon emissions. This study also presented the carbon emission trajectory under different scenarios; from the simulation results, environmental regulation measures such as accelerating industrial structure transformation and increasing energy consumption intensity may help to achieve the peak carbon emission target as soon as possible. It also suggests that uncertainty should be included in future carbon assessments to present a more complete carbon emission trajectory.
China has committed to decreasing its emission intensity by 60% to 65% by 2030 compared to 2005 levels and achieving carbon neutrality by 2060. It is of great importance to evaluate the CO2 emission reduction potential to quantify the amount of CO2 emissions that can be less generated and the amount that should be balanced out. Economic structure adjustment and CO2 emission efficiency improvement will contribute to mitigating CO2 emissions, which always happen simultaneously in the real world. However, few studies consider these issues simultaneously, which can lead to inaccurate estimation. A scenario analysis framework is proposed to estimate their combined effects, and an indicator is proposed to measure the technical feasibility of achieving the reduction potential. A set of scenarios are designed based on this framework and we find that: (1) to achieve carbon neutrality, 6161.16 Mt of CO2 emissions of China can be less generated compared to 2017 levels by significantly increasing its tertiary industry share to high-income entities’ level and adopting the most advanced technology to improve emission efficiency; the remaining 2732.40 Mt of CO2 emissions should be removed by carbon offsetting. Regarding emission intensity, 81.39% can be reduced compared with the 2005 level; and (2) Technical feasibility analysis shows Sichuan, Chongqing, and Anhui have the largest technical barriers in achieving the reduction potential. The proposed scenario analysis framework can provide a reference not only for China to achieve the emission mitigation pledges, but for countries with significant technological differences and structure adjustment to formulate mitigation strategies.
This paper explores the features of accounting for the national carbon emission inventory under four different system boundaries, including the presently operational territorial production-based accounting (PBA) practised by the UNFCCC. Using a recently published input-output table with the base year for 2013–2014, the study calculates India’s ‘Nationally Determined Contribution (NDC)’ to the mitigation of carbon emissions using production-based, consumption-based, sharing-based and equity-based accounting. The study finds India as a net importer of carbon emissions, especially for its high emission-intensive capital and energy goods import such that the country is bearing the minimum burden of emission reduction responsibility. However, the study intends to analyse this burden of responsibility in terms of the criteria of justice and effectiveness and addresses the future policy priorities for India to become a net exporter of industrial goods. We recommend India for supporting and implementing an alternative system boundary of emission inventory accounting which would be more consistent with the provisions of ’equity’ subscribed under the UNFCCC while at the same time, helpful for achieving the climate goals by appropriately trace and track the sources of ‘carbon leakage’.
The expansion of Xi’an City has caused the consumption of energy and land resources, leading to serious environmental pollution problems. For this purpose, this study was carried out to measure the carbon carrying capacity, net carbon footprint and net carbon footprint pressure index of Xi’an City, and to characterize the carbon sequestration capacity of Xi’an ecosystem, thereby laying a foundation for developing comprehensive and reasonable low-carbon development measures. This study expects to provide a reference for China to develop a low-carbon economy through Tapio decoupling principle. The decoupling relationship between CO2 and driving factors was explored through Tapio decoupling model. The time-series data was used to calculate the carbon footprint. The auto-encoder in deep learning technology was combined with the parallel algorithm in cloud computing. A general multilayer perceptron neural network realized by a parallel BP learning algorithm was proposed based on Map-Reduce on a cloud computing cluster. A partial least squares (PLS) regression model was constructed to analyze driving factors. The results show that in terms of city size, the variable importance in projection (VIP) output of the urbanization rate has a strong inhibitory effect on carbon footprint growth, and the VIP value of permanent population ranks the last; in terms of economic development, the impact of fixed asset investment and added value of the secondary industry on carbon footprint ranks third and fourth. As a result, the marginal effect of carbon footprint is greater than that of economic growth after economic growth reaches a certain stage, revealing that the driving forces and mechanisms can promote the growth of urban space.
A principal component analysis based on the generalized Gini correlation index is proposed (Gini PCA). The Gini PCA generalizes the standard PCA based on the variance. It is shown, in the Gaussian case, that the standard PCA is equivalent to the Gini PCA. It is also proven that the dimensionality reduction based on the generalized Gini correlation matrix, that relies on city-block distances, is robust to outliers. Monte Carlo simulations and an application on cars data (with outliers) show the robustness of the Gini PCA and provide different interpretations of the results compared with the variance PCA.
Personal carbon budgets and trading (PCT) is an innovative and ground-breaking downstream policy, which locates rights to and responsibilities for emissions at the individual level. In addition to emission reductions, it may also provide a method for earning and evening out the income distribution. The economic impacts of PCT for a single person depend greatly on the method of allocating emission rights. Currently, it is not known how the economic impacts for various income levels differ between different allocation methods. Herein, we present a PCT model for mobility and various methods for allocating emissions rights. The economic impacts on various income levels are calculated based on Finnish statistical mobility data and using a PCT pilot in the city of Lahti. Results show that greenhouse gas emissions from mobility are lower for people with a low income, and PCT could provide additional income and even out the income distribution. The financial benefits and payoffs depend on the carbon cap, carbon floor price and emission right allocations. The results of this study provide basic information on how PCT can be put into action, what options there are for emission allocation, and how PCT can be used to even out the income distribution.
To achieve the goal of carbon dioxide emission reduction in 2030 promised to the United Nations, China unified the Carbon Trading System (CTS) in 2017 since carbon dioxide quota allocation is one of the core issues of carbon trading. It is imperative to establish a flexible carbon quota allocation system based on the unbalanced characteristics of resource endowment and economic development in different regions. Unlike previous distribution research, this paper considers five principles, which are fairness principle, efficiency principle, feasibility principle, development principle, and innovation principle. The maximum deviation method is used to research the carbon emission quota allocation in 30 provinces of China, and the results are compared with those under the single principle and the information entropy method. The results reveal that the distribution under the single principle is severely unbalanced, making the region have a strong sense of relative deprivation. The maximum deviation method is better than the information entropy method to achieve carbon intensity by 2030. It is also conducive to promote the coordinated development of the regional economy, narrow the poverty gap, and achieve sustainable development.
Establishing a fair and reasonable initial allocation scheme for carbon emission rights is a prerequisite for the efficient and stable operation of the carbon market and an important guarantee for China to achieve its emission reduction target. This study considers the historical transfer of carbon emissions and measures the cumulative net carbon emissions of China's provinces based on the principle of consumer responsibility. It draws up an initial allocation plan for carbon emission rights in China's provinces in 2020, based on the principles of equity, efficiency, and sustainability. The carbon emission and carbon intensity efficiencies of the provinces under this distribution scheme were also evaluated. The results show that the historical net carbon export area mainly consists of energy-intensive provinces and economically underdeveloped poor and remote provinces. Additionally, the historical net carbon import area mainly consists of energy-starved and economically developed provinces. Provinces with high-value ecological services (Xinjiang, Heilongjiang, and Inner Mongolia) or with large economies and dense populations (Shandong, Guangdong, and Sichuan) will receive higher carbon emission rights, while those with developed economies (Shanghai, Beijing, and Tianjin) will receive lower carbon emission rights. Provinces with a good ecological environment (Inner Mongolia, Heilongjiang, and Xinjiang) as well as certain economically underdeveloped western provinces (Chongqing, Sichuan, and Yunnan) will have abundant carbon space in the future. Provinces with rapid economic development (Beijing, Shanghai, and Jiangsu) and resource-based provinces (Shanxi and Liaoning) with a high historical carbon output have little carbon space. Considering the historical carbon transfer, the carbon emission rights allocation system will benefit resource-based industrial provinces (Shanxi, Inner Mongolia, and Liaoning) and less developed western provinces (Shaanxi, Ningxia, and Xinjiang) in improving their carbon emission efficiency. This study provides a new research idea for formulating a more equitable and efficient allocation scheme for carbon emission rights.
Thinking about justice can enhance or impede forgiveness of others. In this study, we show that these effects crucially depend on tendencies to believe in justice. We assessed beliefs about distributive and procedural justice for self and others among university students from the Midwestern United States. We then primed participants to think about distributive or procedural justice, either for self or others. We measured general forgiveness attitudes, as well as motivations to forgive a past transgression. Among participants who strongly believed in distributive justice for others, forgiveness was attenuated by thinking about distributive justice for others (congruence-inhibition), but accentuated by thinking about distributive justice for self, or procedural justice for others (incongruence-facilitation). Among participants who strongly believed in procedural justice for others, forgiveness was accentuated by thinking about procedural justice for self or distributive justice for others (incongruence-facilitation). Results highlight contextualized rather than rote effects of justice on forgiveness.
Future trends in air pollution and greenhouse gas (GHG) emissions for China are of great concern to the community. A set of global scenarios regarding future socio-economic and climate developments, combining shared socio-economic pathways (SSPs) with climate forcing outcomes as described by the Representative Concentration Pathways (RCPs), was created by the Intergovernmental Panel on Climate Change (IPCC). Chinese researchers have also developed various emission scenarios by considering detailed local environmental and climate policies. However, a comprehensive scenario set connecting SSP–RCP scenarios with local policies and representing dynamic emission changes under local policies is still missing. In this work, to fill this gap, we developed a dynamic projection model, the Dynamic Projection model for Emissions in China (DPEC), to explore China's future anthropogenic emission pathways. The DPEC is designed to integrate the energy system model, emission inventory model, dynamic projection model, and parameterized scheme of Chinese policies. The model contains two main modules, an energy-model-driven activity rate projection module and a sector-based emission projection module. The activity rate projection module provides the standardized and unified future energy scenarios after reorganizing and refining the outputs from the energy system model. Here we use a new China-focused version of the Global Change Assessment Model (GCAM-China) to project future energy demand and supply in China under different SSP–RCP scenarios at the provincial level. The emission projection module links a bottom-up emission inventory model, the Multi-resolution Emission Inventory for China (MEIC), to GCAM-China and accurately tracks the evolution of future combustion and production technologies and control measures under different environmental policies. We developed technology-based turnover models for several key emitting sectors (e.g. coal-fired power plants, key industries, and on-road transportation sectors), which can simulate the dynamic changes in the unit/vehicle fleet turnover process by tracking the lifespan of each unit/vehicle on an annual basis. With the integrated modelling framework, we connected five SSP scenarios (SSP1–5), five RCP scenarios (RCP8.5, 7.0, 6.0, 4.5, and 2.6), and three pollution control scenarios (business as usual, BAU; enhanced control policy, ECP; and best health effect, BHE) to produce six combined emission scenarios. With those scenarios, we presented a wide range of China's future emissions to 2050 under different development and policy pathways. We found that, with a combination of strong low-carbon policy and air pollution control policy (i.e. SSP1-26-BHE scenario), emissions of major air pollutants (i.e. SO2, NOx, PM2.5, and non-methane volatile organic compounds – NMVOCs) in China will be reduced by 34 %–66 % in 2030 and 58 %–87 % in 2050 compared to 2015. End-of-pipe control measures are more effective for reducing air pollutant emissions before 2030, while low-carbon policy will play a more important role in continuous emission reduction until 2050. In contrast, China's emissions will remain at a high level until 2050 under a reference scenario without active actions (i.e. SSP3-70-BAU). Compared to similar scenarios set from the CMIP6 (Coupled Model Intercomparison Project Phase 6), our estimates of emission ranges are much lower than the estimates from the harmonized CMIP6 emissions dataset in 2020–2030, but their emission ranges become similar in the year 2050.
Despite China’s emissions having plateaued in 2013, it is still the world’s leading energy consumer and CO2 emitter, accounting for approximately 30% of global emissions. Detailed CO2 emission inventories by energy and sector have great significance to China’s carbon policies as well as to achieving global climate change mitigation targets. This study constructs the most up-to-date CO2 emission inventories for China and its 30 provinces, as well as their energy inventories for the years 2016 and 2017. The newly compiled inventories provide key updates and supplements to our previous emission dataset for 1997–2015. Emissions are calculated based on IPCC (Intergovernmental Panel on Climate Change) administrative territorial scope that covers all anthropogenic emissions generated within an administrative boundary due to energy consumption (i.e. energy-related emissions from 17 fossil fuel types) and industrial production (i.e. process-related emissions from cement production). The inventories are constructed for 47 economic sectors consistent with the national economic accounting system. The data can be used as inputs to climate and integrated assessment models and for analysis of emission patterns of China and its regions.
China’s CO2 emissions have plateaued under its commitment to reaching peak carbon emissions before 2030 in order to mitigate global climate change. This commitment is aligned with China’s turn toward more sustainable development, named “the new normal” phase. This study aims to explore the role of possible socioeconomic drivers of China’s CO2 emission changes by using structural decomposition analysis (SDA) for 2002–2017. The results show deceleration of China’s annual emissions growth from 10% (2002–2012) to 0.3% (2012–2017), which is mainly caused by gains in energy efficiency, deceleration of economic growth, and changes in consumption patterns. Gains in energy efficiency are the most important determinants, offsetting the increase by 49% during 2012–2017. The recent moderation of emission growth is also attributed to China’s decelerating annual growth rate of gross domestic product (GDP) per capita from 12% (2002–2012) to 6% (2012–2017) and to the economic transformation to consumption-led patterns in the new normal phase.
The rapid economic development has highlighted the global climate change problem and carbon dioxide emissions have brought challenges to global climate change. The combined carbon emissions of the United States and China are nearly half of global carbon emissions. These two countries have made great contributions to environmental protection and responded actively to global warming, and set the goal of carbon neutrality. This study takes the United States and China as examples to compare their national paths and policies to achieve carbon neutrality while also analyzing the stage effects of carbon emission reduction in these two countries. This study found that the policy systems in the United States and China are opposites of each other. The United States is a “Bottom to Top” system while China is a “Top to Bottom”. The CO2 emission in the United States is currently in the absolute carbon emission reduction period, that is, the reduction of total carbon emissions; while China is in the relative carbon emission reduction period, that is, the reduction of carbon emission intensity. China's transition time from carbon peaking to carbon neutrality is shorter than that of the United States, which is a huge challenge for China because its population is much larger than that of the United States. The results of this research can be used by other countries and regions for supporting carbon reduction policy decision-making and achieving UN sustainable development goals (SDGs).
Purpose
This paper aims to explore the future path of agricultural development in China toward 2060 under the dual carbon goals, so as to inform better policy choices for facilitating agricultural and rural transformation toward the goal of maintaining food security, sustainable income growth and low carbon emission.
Design/methodology/approach
This study employs a single-country, multi-sectoral computable general equilibrium model, CHINAGEM model and develops eight illustrative scenarios to simulate the impacts of attaining dual carbon goals on agricultural development in China. Additional two scenarios have also been designed to inform better policy making with the aim to offset the negative impact of the decarbonization schemes through facilitating agricultural technology progress.
Findings
Dual carbon goals are projected to impose substantial negative impact on agricultural productions and consumptions in China in the coming four decades. Under the assumption of business as usual, agricultural production will reduce by 0.49–8.94% along with the attainment of carbon neutrality goal by 2060, with the production of cereals and high-value being more severely damaged. To mitigate the adverse impact of the decarbonization schemes, it is believed that fastening technology progress in agriculture is one of the most efficient ways for maintaining domestic food security without harming the dual carbon goals. In particular, if agricultural productivity (particularly, for cereals and high-value products) can be increased by another 1% per year, the production losses caused by carbon emission mitigation will be fully offset. This implies that promoting technology progress is still the best way to facilitate agricultural development and rural transformation in future China.
Originality/value
The paper contributes to the literature in better informing the impact of dual carbon goals on China's agriculture and the effectiveness of technology progress in agriculture on buffering the adverse impact of the decarbonization schemes and promoting agricultural development.
The decline in coal production and the increase in low-carbon production levels are both important manifestations of the industry's elimination of backward production capacity. To test the benign interaction between the “quantity” and the “quality”, this paper takes 12 provinces in China with significant reductions in coal production, calculates and compares the changes in the coupling coordination degree (CCD) of the two systems from 2000 to 2019, and explore the key factors that affect CCD. The results show that from 2013 to 2019, 12 provinces have seen a decline in coal production between 5% and 23.6%, and the effectiveness of coal production control is relatively significant. However, the carbon emission reduction efficiency of the coal industry in 9 of these provinces has been at a lower level with a mean value of 0.481. After 2013, the CCD of 12 provinces significantly improves and the two systems have converged to a good state of coordination (Dit>0.6) in most provinces by 2019. However, the high volatility of CCD after 2013 suggests the instability of interaction. LnPGDP PE POV, and FDI are proven to be the key factors affecting the system coordination. Meanwhile, other factors need to be further explored and improved.
Using a production function approach, this study is the first to focus on the effect of operational inefficiency on the stock returns of large global financial firms. Inefficiency is inversely related to returns for systemically important firms and is robust to including asset pricing factors, firm-specific variables, and other controls. Further, this finding is robust to the inclusion of a firm's profitability and idiosyncratic risk. Thus, the inefficiency metric is not just an alternative way to measure a firm's profitability or risk. Instead, inefficiency can be an important factor to consider when evaluating a large financial firm's stock returns.
Personal carbon allowances have been of considerable interest in environmental research in the last decade, yet no policy implementations have been adopted, partly due to uncertainty around the political acceptability of equal allowances. We tackled this issue by surveying public perceptions of fairness in carbon allowance allocation in urban mobility. Qualitative and quantitative inquiry data of 304 respondents were analysed statistically and thematically. Three distributive principles according to equity perceptions of equality, equity by capability, and equity by responsibility, were examined. The distribution of personal carbon allowances, which is sensitive to differing needs and capabilities, was perceived fairer than the other proposed options. The allocation preference differed according to isolationist and integrationist approaches, where both equity by capability and equity by responsibility evoked talk of benefits and burdens that could be produced by emission rights, while those who alluded to equality were interested in the distribution of emission rights as such. Prioritising needs and capabilities demonstrated that questions of managing the daily life still edge ahead of climate questions in many citizens eyes. Therefore, policy propositions should focus more on the shared mobility practices of everyday life and how they could be supported and incentivised towards sustainability. We concluded that due to the lack of absolute distributive consensus, policies gain legitimacy by procedural equity, and including citizens in decision making.
In achieving the “double carbon” target, the emission trading system (ETS), as a key market-based emission reduction mechanism, assumes the responsibility of addressing climate change and implementing reform. Most studies focus only on the emission reduction effects of green policy, but weighing the relationship between emission reduction and development is a practical dilemma to be faced. Taking China's pilot ETS as an example, this problem was analyzed using the synthetic control method, and the specific impact channels are discussed. It was found that: (1) Carbon emissions and carbon emission intensity in the pilot ETS areas of Tianjin, Hubei and Chongqing decreased significantly, and economic growth has progressed to a certain extent. Robustness tests basically verified the effectiveness of the policy implementation. (2) The pilot ETS mainly has an impact through the transformation and upgrading, and technological innovation channels, promoting significant improvements in energy structure, industrial structure, energy efficiency and the number of patent applications. This study releases a positive signal for the compatibility of emission reduction and economic development goals at the national level for China's pilot ETS.
A fair regional CO2 allocation scheme is an important guarantee to achieve carbon peak and carbon neutrality. Most existing studies on CO2 allocation focus on carbon sources while ignore the role of carbon sinks. By combining the carbon sinks estimated by remote sensing technology with the carbon sources identified from multiple dimensions, this paper first analyzes the characteristics of China's net CO2 emissions in 2015. Then a centralized DEA model is developed to construct a net CO2 allocation scheme in 2020 and 2030 using historical data in 2015 with the help of the Python programming. On this basis, the allocation schemes considering carbon sinks or not are further compared to verify the rationality of the proposed allocation results. It concludes that a net CO2 allocation scheme considering carbon sinks delivers significantly better results as it eases the dispersion of inter-provincial carbon quota and balances the development among regions. Geographically, Southwest China, rich in carbon sinks but less developed, will be given more quotas if carbon sinks are included than if they are not, while North China, poor in carbon sinks but more developed, will assume more responsibility for reducing emissions. Our paper sheds light on the importance of incorporating carbon sinks into the CO2 allocation issue.
Protecting the ecological environment is the common task of all mankind, and it is difficult for any country to achieve its goals alone. As matters stand, more than 130 countries and regions have proposed “zero carbon” or “carbon neutral” climate goals. This study established a three-stage carbon emission analysis framework for 29 countries that have proposed carbon neutrality targets. First, the carbon emissions of each country are decomposed based on Logarithmic Mean Divisia Index (LMDI) method. Then, the decoupling relationship between carbon emissions and economic development of each country is analyzed based on the Tapio model. Finally, according to the current and historical decoupling status, the future carbon emission trends of various countries are studied. The results indicated: (1) Energy intensity and economic growth are key drivers of carbon emissions. (2) There are certain differences in the decoupling status of countries during each period. Of which, the United States performed best, followed by the four countries whose climate goal process is in law (France, the United Kingdom, Hungary and New Zealand). (3) No matter in current decoupling state scenario or historical decoupling state scenario, the emission reduction effects of various countries are not satisfactory. Few countries can successfully achieve NDC targets or carbon neutral goals.
To balance China's socio-economic development and emission reduction goals, a fair and effective provincial carbon emission allowance (CEA) allocation is necessary. By considering the implied emissions of inter-provincial power transfer, this study designed a dynamic multi-criteria CEA allocation model based on four criteria—egalitarianism, historical responsibility, emission reduction capability, and emission efficiency—to calculate the provincial CEA year by year before 2030. The efficiency and fairness of the CEA scheme were evaluated through the Data envelopment analysis (DEA) model, the environmental Gini coefficient, and its grouped decomposition method. The national overall CEA, the results revealed, will peak during the 15th Five-Year Plan (FYP) period. Specifically, the CEA for eastern and central China is expected to peak first during the 14th FYP period, while the northeast region's CEA remains stable and that of the western region continues to grow. Provinces with high carbon emissions, high carbon emission intensity and high per capita carbon emissions and provinces with particularly high carbon emissions will face great pressure regarding emission reduction, and their CEA peaks are expected to arrive before 2025 and 2030 respectively. The CEA of the less-developed provinces will have a surplus. In terms of time, the high-emission provinces face greater emission reduction pressure during the 15th FYP period than during the 14th FYP period. In terms of scheme evaluation, the scheme achieved a double improvement in fairness and efficiency compared with the current actual emissions of various provinces. Reducing the differences in per capita CEA between the different regions and provinces in the western and eastern regions will help improve the scheme's fairness. This study overcomes the existing researches' shortcomings on the large differences in the distribution of emission reduction pressures in key provinces and is more feasible in practice.
To solve the multi-modal multi-objective optimization problems in which the same Pareto Front (PF) may correspond to multiple different Pareto Optimal Sets (PSs), an improved multi-objective particle swarm optimizer with short-term memory and K-means clustering (MOPSO-SMK) is proposed in this paper. According to the framework of multi-objective particle swarm optimization (MOPSO) algorithm, the designs of updating mechanism and population maintenance mechanism are the keys to obtain the optimal solutions. As a significant influence factor of the updating mechanism, the inertia weight has been discussed in this paper. In the improved algorithm, a new update model for the value of pbest based on short-term memory is proposed. The update strategies based on K-means clustering are adopted to obtain the better gbest and elite archive. 16 multi-modal multi-objective optimization functions are used to verify the feasibility and effectiveness of the proposed MOPSO-SMK. As the results show, MOPSO-SMK has more advantages in four indexes (1/PSP, 1/HV, IGDX, and IGDF) compared with other three multi-objective optimization algorithms.
Load controller design for floating wind turbine systems (FWTS) poses unique challenges owing to the couplings of platform motions, blade loads and rotor power. Any controller based on a single objective is likely to worsen other performance metrics. This paper presented a data-driven control system to comprehensively handle that muti-objective coupling problem. The control system was realized by introducing a model free adaptive control (MFAC) based on deformable trailing edge flap (DTEF) and a feedback compensation in the blade pitch control loop. The identification error and actuator saturation were considered while designing the control law. The proposed control system was numerically tested in the modified FAST code equipped with DTEF interfaces. Numerical results showed considerable load reductions on blades and floating platforms at the cost of the added DTEF actuation and a minor increase in blade pitch rate. Moreover, the generator power fluctuations and the fatigue loading on drive train and tower were also alleviated. In addition, the synchronous aero-hydro-elastic coupling relationships in the wind-blade-wave-platform system were impaired by introducing the servo-dynamics of DTEF and blade pitch feedback compensation. Hence the total energy flowing into FWTS was dissipated.
Relative income deprivation (RID) is a known risk factor for poor health. Previous research has proposed several measures to assess RID, e.g., Income Rank and the Yitzhaki Index. Hounkpatin et al. (2020) presented a new approach – the CR˜i index – to account for the observation that individuals are more sensitive to the differences in incomes of others who are closer to them, rather than to comparisons with incomes of others far above them. Using a Japanese nationwide cohort of older adults (n = 62,438; mean [SD] age: 73.0 [5.6] years), this study compared the performance of alternative indices of RID in predicting health outcomes (depressive symptoms, functional capacity, and self-rated health), as well as the use of alternative CR˜i index weights (α weight range: −0.9 to 0.9). When 0<α<1, higher income differences lead to a more significant increase in relative deprivation, while when −1< α <0, excessively high incomes contribute less to the relative deprivation of lower income individuals in the same reference group. Results showed that all measures of relative income deprivation were associated with deteriorating mental and physical health among older Japanese adults. However, while the CR˜i index consistently outperformed the Yitzhaki Index, this did not hold true invariably when compared to the Income Rank – depending on the health outcome and the reference group. Also, while negative α parameters showed a good statistical fit in most models, the findings were not conclusive – the best-fitting CR˜i weight parameters ranged from −0.9 to 0.9. Therefore, a clear direction for the contribution of higher incomes to relative deprivation could not be established based on the study population.
From the perspective of low-carbon economy(LCE), this paper explores the optimal structure adjustment strategy, emission reduction potential and utilization efficiency of fossil energies (coal, petroleum, and natural gas) for 30 provinces in China from 2010 to 2019. A mixed integer data envelopment analysis model is proposed to identify the optimal structure adjustment and the potential emission reduction. Then, a new total-factor energy efficiency (TFEE) index based on energy structure adjustment is constructed to statically examine the utilization efficiencies of the three fossil energies. Finally, a novel calculation method of global Malmquist index is presented to dynamically analyze the energy productivity change. The results demonstrate that more than half of the provinces have the potential to reduce coal consumption, while for petroleum and natural gas, the optimal adjustment of different provinces is diverse. The CO2 emission reduction brought about by the energy structure adjustment accounts for about 11% of the emission, and the emission reduction potential of central and western regions is significantly larger than that of eastern region. Technological progress is the main driving of productivity improvement for both LCE and three fossil energies. However, significant differences can be found in the management level of different fossil energies among provinces.
Carbon emission quota allocation plays a critical role in carbon emission reduction in an efficient and economic manner. However, the scientific assessment of carbon quota allocation schemes and thus making a reasonable decision depending on preference still confuses relevant decision makers. This study proposes a performance analysis framework for carbon quota allocation schemes by using a nonparametric frontier analysis approach from the perspectives of economic growth and energy conservation. Then, we apply this approach to evaluate the relative economic and energy conservation performances of 15 allocation schemes by reallocating the 2015 Chinese provincial carbon emission quotas, each of which is constructed via a combination of equity, grandfathering, efficiency and ability to pay principles. The results show that the proposed allocation scheme that integrates efficiency and the ability to pay principles is the best option, realizing approximately 6.78% outputs increase and 3.01% energy conservation relative to the actual 2015 emission scenario. We also show that the best scheme is linked to total quotas; the scheme considering only the equity principle is superior to others, while the total quotas are reduced by 10%. The study provides not only a theoretical tool, but also empirical evidence for the construction of China's carbon quota allocation scheme.
International climate policy debate has been struggling to define an agreeable principle for just distribution of the burdens of reducing greenhouse gases. By the integrated assessment modeling, we investigate climate change mitigation pathways compatible with the Paris Agreement goals under different carbon budget allocation principles, namely, cost-effectiveness, equality, and grandfathering for 11 world regions. The results indicate that regional carbon quotas remain similar under equality and grandfathering principles, except for regions with a high population projection, i.e., Sub-Saharan Africa and South Asia, or regions with a high level of current emissions, such as North America. Irrespective of the burden-sharing principles, most world regions must reach carbon neutrality before 2070 to meet the 2°C climate targets. Moreover, carbon emissions reduced by carbon capture and storage applications are found to peak around 2050. In general, the share of fossil fuels in primary energy is larger under the larger carbon quota, which will also increase carbon capture and storage applications in fossil fuels. The average global investment needs for mitigating climate change are the lowest under the cost-effectiveness principle, and the marginal abatement cost is the same for all regions. For each region, as the carbon quota decreases the marginal abatement cost increases, while the energy investment changes independently from the carbon quota. These insights can inform global climate change negotiations and help policymakers formulate timelines for carbon neutrality, energy transition, and technology deployment and investment portfolios.
In this work, the models of desulfurization and denitrification are added to solve the problem of SO2 and NOX standardized discharge. The design and optimization strategy of steam power system (SPS) considering contaminant emissions reduction technology is proposed to achieve the trade-off between economic and environmental goals. Detailed superstructure networks of desulphurization based on wet limestone flue gas desulfurization and denitrification based on selective catalytic reduction were established and embedded in the SPS model. Then, based on this combined superstructure model, a mathematical formulation of multiple objective mixed integer nonlinear programming describing the SPS coupled with desulfurization and denitrification was established. The steam flow rate, outlet enthalpy, the consumption of the turbine power of the direct drive equipment and the electricity generated by the turbine, the flow rate and efficiency of desulphurization and denitrification are chosen as the optimization variables. The operating conditions and equipment parameters of the global system are optimized. Finally, the second-generation non-dominated sorting genetic algorithm (NSGA-II) was applied to obtain the Pareto optimization curve, exploring trade-offs between economic and environmental goals. Two case studies are used to assess the applicability and performance of the optimization formulation and solution algorithm.
In order to ensure the implementation of the emission trading scheme (ETS), the allocation of CO2 emission quotas in China's provinces is of significant importance. Unlike previous studies that allocated CO2 emission quotas directly to Chinese provinces, we propose a two-step allocation scheme of “country to region” and “region to province” to formulate the provincial CO2 emission quotas allocation. In the first step, considering the regional coordination development, the Shapley value method is used to assign the CO2 emission quotas to China’s eastern, central, and western regions by simulating the regional collaborative abatement. In the second step, we use the entropy method to obtain the initial CO2 emission quotas for each province combining the principles of fairness, efficiency, feasibility, and sustainability. Then, a zero-sum gains data envelopment analysis (ZSG-DEA) model is applied to evaluate initial allocation efficiency and reallocate the CO2 emission quotas to realize efficiency optimization within the region. The results show that the eastern region obtains the largest CO2 emission quotas, followed by central and western regions. The energy-abundant and economic-prosperous provinces, such as Shandong, Hebei, Inner Mongolia, and Xinjiang, face great pressures on CO2 reduction. Compared with existing literature, the proposed scheme’s regional allocation results are more balanced, which will narrow the gaps of regional economic development. Since the emission reduction pressure varies in provinces, developing differentiated policies is critical to realizing China’s reduction targets. By considering regional fairness, this paper provides a reference for allocating CO2 emission quota among provinces to improve adaptability to the current conditions of China.
The determinants of adoption of technologies are mostly focused on socioeconomic and demographic characteristics of farmers, overlooking the impact of corruption and preferential treatments (partisanship, nepotism and tribalism). We extend technology adoption predictors to include preferential treatment, and the results are explained with Relative Deprivation Theory. We used survey data collected from participants and non-participants of Planting for Food and Jobs (PFJ) programme in 2019. Respondents were rice farmers from three regions (Northern, Savannah and NorthEast regions) of Northern Ghana. We analysed the data using Systematic Probit Regression model after satisfying variables differential and correlation assumptions. The results revealed that while partisanship and tribalism are significant inverse factors, corruption is an insignificant negative determinant of participation in PFJ. We find nepotism to have a strong positive correlation with participation in PFJ. We recommend that government should plug all the loopholes facilitating corruption and preferential treatment if it intends to increase participation and rice productivity effectively. "Success countries leaders ride advance technology in agriculture to achieve universal food objective and heading towards zero hunger. Leaders of fail nations implemented "equal" policies and experience food insecurity at the apex. The vision in the world succeeds in developed countries with the majority living improved living standard. Leaders of rural nations could make hunger and poverty a history" [1].
A large number of intelligent algorithms based on social intelligent behavior have been extensively researched in the past few decades, through the study of natural creatures, and applied to various optimization fields. The learning-based intelligent optimization algorithm (LIOA) refers to an intelligent optimization algorithm with a certain learning ability. This is how the traditional intelligent optimization algorithm combines learning operators or specific learning mechanisms to give itself some learning ability, thereby achieving better optimization behavior. We conduct a comprehensive survey of LIOAs in this paper. The research includes the following sections: Statistical analysis about LIOAs, classification of LIOA learning method, application of LIOAs in complex optimization scenarios, and LIOAs in engineering applications. The future insights and development direction of LIOAs are also discussed.
China has promised that CO2 emissions per GDP in 2030 would decrease by 60% to 65% than that in 2005, and proposed the goal of achieving carbon neutral by 2060. In order to fulfill these goals, Carbon Emissions Trading (CET) and Tradable Green Certificates (TGC) have been implemented in the power industry during the ‘13th five-year’ period. We firstly simulate the combined effects of TGC and CET on the electricity market from 2020 to 2026. Further, we build a policy synergy model to explore the optimization relationship between TGC and CET systems. The results show that, the power supply structure can be optimized under TGC and CET systems. The growth rate of CO2 emissions from the power industry will slow down, accelerating peaking CO2 emissions of the power industry. The national CO2 emissions reduction goal (1.185–1.037 tons /RMB 10,000 yuan by 2030) is expected to be achieved. There may be policy redundancy between TGC and CET systems. It is determined by how to set renewable energy objective and CET quota objective. Under multiple policy objectives, the key is to obtain the policy synergy intervals for staged optimization. Finally, we propose some suggestions on the improvement of TGC and CET mechanisms, and combined implementation and optimization of multiple emission reduction policies.
As one of the most vulnerable sectors exposed to the COVID-19 pandemic, transport sectors have been severely affected. However, the shocks and impact mechanisms of infectious diseases on transport sectors are not fully understood. This paper employs a multi-sectoral computable general equilibrium model of China, CHINAGEM, with highly disaggregated transport sectors to examine the impacts of the COVID-19 pandemic on China’s transport sectors and reveal the impact mechanisms of the pandemic shocks with the decomposition analysis approach. This study suggests that, first, multiple shocks of the COVID-19 pandemic to transport sectors are specified, including the supply-side shocks that raised the protective cost and reduced the production efficiency of transport sectors, and the demand-side shocks that reduced the demand of households and production sectors for transportation. Second, the outputs of all transport sectors in China have been severely affected by the COVID-19 pandemic, and passenger transport sectors have larger output decreases than freight transport sectors. While the outputs of freight transport sectors are expected to decline by 1.03–2.85%, the outputs of passenger transport sectors would decline by 3.08–11.44%. Third, with the decomposition analysis, the impacts of various exogenous shocks are quite different, while the changes in the output of different transport sectors are dominated by different exogenous shocks. Lastly, while the supply-side shocks of the pandemic would drive output decline in railway, waterway, and aviation transport sectors, the demand-side shocks would drive so in the road, pipeline, and other transport sectors. Moreover, the COVID-19 pandemic has negative impacts on the output of most non-transport sectors and the macro-economy in China. Three policy implications are recommended to mitigate the damages caused by the COVID-19 pandemic to the transport sectors.
To improve the optimization efficiency of the intelligent bionic optimization algorithm, this paper proposes intelligent bionic optimization algorithm based on the growth characteristics of tree branches. Firstly, the growth organ of the tree is mapped into the coding of the tree growth algorithm (intelligent bionic optimization algorithm). Secondly, the entire tree, that is the growing tree, is formed by selecting the individual that grows fast to generate the next level of shoot population. Lastly, if the growing tree reaches a certain level, the individual coding of the shoots is added to enhance the searching ability of the individuals of current generation in the growth tree growth space, so that the algorithm approaches the optimal solution. The experimental results were compared with the optimization results of the genetic algorithm and the ant colony algorithm using the classic optimization function and showed that this algorithm has fewer iterations, a faster convergence speed, higher precision, and a better optimization ability than the genetic algorithm and the ant colony algorithm.
How to share responsibility for greenhouse gas emissions between consumers and producers is a highly sensitive question in international climate policy negotiations. Traditional ‘Production-Based Accounting’ (PBA), which assigns responisibility to the region where emissions are released, has frequently been challenged by ‘Consumption-Based Accounting’ (CBA) schemes that suggest that greenhouse gas emissions generated to produce traded goods and services should be attributed to their final consumers. PBA and CBA both lack a sound foundation in economic theory as they do not consider the economic benefits accruing to producers or consumers if carbon emissions do not carry a price that reflects their social costs. We build on well-established economic theory to derive how to share responsibility for trade-related emissions between producers and consumers and apply this novel approach for the most prominent bilateral trade relationships using multi-regional input–output data. We propose an ‘Economic Benefit Shared Responsibility’ (EBSR) scheme, in which China is attributed significantly higher responsibility for emissions than in CBA, while lower emissions and responsibility are attributed to both the US and the EU.
Spatial differences in CO 2 emissions must be taken into account in CO 2 mitigation. In this work, a spatial within− between logarithmic mean Divisia index decomposition model was developed by using cluster analysis to evaluate the potential role of fiscal decentralization in driving interprovincial differences in CO 2 emissions in China. The results revealed that the direct impact of fiscal decentralization emerged as a major emission driver after 2009. The differences of provincial CO 2 emissions from the national average can be mainly attributed to emission differences between the distinct provincial clusters. The direct and indirect impacts of fiscal decentralization contributed to the shaping of differences in CO 2 emission between provinces and their provincial cluster average, and between provincial cluster average and the national average. Reducing the differences in CO 2 emission between distinct provincial clusters should be considered a breakthrough for the Chinese government. The provinces with CO 2 emissions below the national average and above the average emissions of its provincial cluster still have the potential for further mitigation. Optimizing the expenditure authority of the central and provincial governments and improving the energy efficiency of the provincial fiscal expenditure are the two effective ways to further promote CO 2 mitigation.
A fair and effective carbon dioxide emissions allowances (CEA) allocation scheme is critical to balance regional development and alleviate social poverty. From the perspective of egalitarianism, this study aims to investigate the Chinese provincial CEA allocation in 2030, as well as the equality of allocation results. For this purpose, a total of 11 allocation schemes based upon a composite indicator method are developed, which consider different combinations of four allocation criteria, namely, egalitarian, responsibility, capability and efficiency. Then, an environmental Gini coefficient derived from Lorenz curves of allocation results is proposed to examine the equality of the results. The main findings show that the egalitarian criterion plays a crucial role in constructing allocation schemes, and different schemes often give rise to diversified allocation results. Although all the 11 allocation schemes, with the environmental Gini coefficients ranging from 0.0642 to 0.4019, better ensure the equality of allocation results, the schemes considering egalitarian criterion present higher equality over the schemes without this criterion. Furthermore, the schemes are likely to present higher equality, when the efficiency criterion is measured by the reciprocal of emission intensity, rather than the indicator derived from a data envelopment analysis method. Emission intensity is a better proxy for efficiency criterion in improving the equality of allocation results.
The allocation of carbon emissions reduction responsibility (CERR) is a fundamental step to carbon emissions trading (CET) market. In China's power industry, regions with divergent power generation efficiencies and energy structures are connected by a nationwide power grid, causing shifts in carbon emissions. We construct a graph restricted cooperative game model for the allocation of CERRs among regions by proposing a novel characteristic function to describe possible minimal carbon emissions in which power generation is prioritized by efficiency. We employ the Myerson value as the solution of the game to capture the structure of the power grid and the power transmission. Our results indicate the following: (1) Power producing regions with high ratio of clean energy such as “Southwest” and “Sichuan and Chongqing” obtain negative shares of CERR. This provides an incentive for them to contribute to carbon emissions reduction. (2) Large power consumers such as “East” and “Bohai Rim” should take larger CERRs because they transfer carbon emissions to the power producing regions. (3) The role of a region in the power transmission network is an important factor in allocating CERR. This study provides insightful policy implications for the construction of a CET market in China's power industry.
China has committed to the international community to achieve its carbon dioxide (CO2) emissions peak around 2030. This article predicts CO2 emissions based on energy consumption to examine the conditions that would lead to achieving China's goal. In order to better understand the relationship between the two, a simple decomposition model decomposes energy consumption into its quantity and structure. Possible trajectories of CO2 emissions in China to the year 2050 depend on three scenario settings with differing total energy consumption and composition. The results indicate that CO2 emissions will not peak in the business-as-usual scenario. CO2 emissions will peak at 10.69 gigatonnes (Gt) in 2030 in the planned energy structure scenario. In the low-carbon energy structure scenario, the peak will occur in 2025 at the value of 10.37 Gt. Not only do slower energy consumption growth rates and the low carbon energy structure enable this peaking to occur earlier in time but also lower the peaking level. China's fossil energy consumption will also peak in 2030 and 2025 in the respective planned and low-carbon energy structure scenarios. The main policy implication is that China's commitment to a CO2 emissions peak is credible and feasible if they slow energy consumption and shift towards lower-carbon fuels.
Environmental deprivation significantly influences urban livability. Previous studies applied spatial data to evaluate environmental deprivation across various neighborhoods, and the identified deprived areas can be directly used in urban planning as areas that need to be addressed. However, perceptions of oneself regarding the local urban environment can influence sense of wellbeing, mental health, and social behavior of this individual; and any adverse feelings from the subjective environmental status can further influence perceived environmental deprivation. This perceived environmental deprivation can be different from the estimation of “objective” environmental deprivation, and perception itself can vary among subpopulations. Absence of consideration of variation in perceived environmental deprivation can lead to a failure of sustainable planning to support all oppressed people affected by urban development.
Therefore, we combined citizens’ perceptions with remote-sensed and administrative data to characterize perceived environmental deprivation among subpopulations, based on a questionnaire with ranks of specific environmental issues under a “city as a whole” concept. Generally, perceived environmental deprivation among subpopulations was driven by different facts. Based on the spatial comparison, self-identified urban residents and people aged >= 30 have faced higher environmental deprivation across the whole city than self-identified rural residents and younger ages. Females, lower income population, and indoor workers have faced with higher environmental deprivation across urban areas than males and higher income population and outdoor workers. These implied that perceived environmental deprivation may be driven by social behaviors of individuals because of social inequality, while planning protocols should be targeted to specific populations to provide comprehensive community support and equity.
Shifting from fossil to clean energy sources is a major global challenge, but in particular for those countries with substantial fossil-fuel reserves and economies depending on fossil-fuel exports. Here we introduce an improved framework for renewable energy planning and decision-making to help such countries to more effectively harness their abundant renewable energy resources. We use Iran as a case for the analysis. The framework includes identifying and removing barriers that prevent the use of renewables. It is based on combining two models: Benefit, Opportunity, Cost, Risk (BOCR) and Analytic Network Process (ANP) models. In the analyses, the mutual weight of strategic criteria is employed such as technology, economy, energy vulnerability, security, global effects, and human wellbeing. Using the integrated model, we find that solar energy would be the preferential renewable energy source for Iran. Also, the role of infrastructures, policies, and administrative structures in renewable energy to facilitate their development was analyzed. The renewable energy policy-making framework presented is applicable to other countries as well.
This paper deduces the connection between Per capita GDP (g) and the Tapio decoupling index (D) as a formula. Then, a two-dimensional decoupling model (Tapio-Z decoupling model) and decoupling analysis framework is constructed based on a Cartesian coordinate system with Per capita GDP (g) as horizontal axis and Tapio decoupling index (D) as vertical axis. Finally, the decoupling status of China's CO2 emissions at provincial level and its dynamic path over the period 2000–2016 is explored. SD (Strong Decoupling) did not occur in sub-period 2000–2005. In the sub-period 2015–2016, the CO2 emissions presented SD-HE (Strong Decoupling-High economic stage) and SD-MHE (Strong Decoupling-Middle and high economic stage) with economic development in 14 regions. During the study period 2000–2016, the decoupling development scores for Beijing, Shanghai, and Tianjin were the biggest. However, the decoupling development score for Xinjiang was the smallest, followed by Guizhou, Ningxia, and Qinghai.