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As a measuring tool of industrial sustainable development, industrial eco-efficiency works as a link between economic benefit and environmental pressure. Industrial agglomeration and energy have always been considered an important influence factor on industrial eco-efficiency. The Chinese government is facing the challenge of reaching a Carbon Peak...
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... to statistical data missing in Tibet, the data set is composed of panel data of 30 provinces from 2009 to 2018 as shown in Table 2, which lists the name, description, unit, symbol, and expected sign of all variables. Table 3 shows the evaluation results of regional IEE, it can be found that there are significant regional disparities in IEE in China. From 2009 to 2018, the mean of IEE in five provinces exceed 1, including Beijing, Tianjin, Shanghai, Jiangsu, and Guangdong, and the efficiency ranking of these effective provinces could be also observed, indicating that it is more scientific to adopt super-efficiency DEA to measure IEE. ...Similar publications
Quantitative studies on how mining activities shape the evolution of regional landscape patterns can contribute to the scientific understanding of how mining cities develop. Based on the theories of life cycle and landscape ecology, this paper takes Jixi, a typical Chinese mining city, as a case study to analyze the landscape pattern features at di...
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... Their findings resonate with our SDM results, suggesting that industrial agglomeration can have varied effects on EE, depending on the agglomeration level and other regional dynamics However, our observations also highlight the complexities of these relationships. Echoing the sentiments of Zhong et al. (2022), our study finds that the impact of industrial agglomeration on EE is multifaceted, influenced by various underlying factors and regional specificities. Similarly, the significance of industrial co-agglomeration and green technological innovations is exemplified by the study conducted by Yang et al. (2022), wherein it is posited that these factors have the potential to positively influence energy efficiency. ...
This paper uses the spatial Bayesian model from 5 regions from 2000 to 2018 to evaluate the spatial panel and hierarchical clustering of regional energy efficiency (EE) in Iran. These findings demonstrate the role of spatial patterns between regional energy efficiency and industrial agglomeration. This has influenced the spatial distribution and energy efficiency performance of Iran’s industries. The empirical findings demonstrate that, based on regional specifications, the administration needs to strategically guide industrial organization to establish within the designated clustering zones. Spatial analyses suggest that industrial agglomeration can enhance regional energy efficiency (EE), although there are noticeable disparities at the regional level. In the eastern region, transportation infrastructure and urbanization positively influence energy efficiency. Findings show that in the central and southern regions, industrial agglomeration has a beneficial effect on boosting EE. In contrast, its effects are less noticeable in the eastern and western regions. Consequently, the industrial agglomeration across the 30 provinces demonstrates a significant spatial correlation with EE. This offers valuable insights for governments when formulating regional industrial policies.
... The second aspect of the study focuses on the examination of the spatiotemporal evolution characteristics of AEE. This investigation primarily employs analytical techniques such as kernel density estimation analysis [13], a spatial autocorrelation model [14,15], and the building of a spatial Markov probability transfer matrix [16]. The third aspect pertains to the examination of the many components that exert influence on AEE. ...
... In this way, the relationship between independent and dependent variables can be interpreted more comprehensively. Therefore, several scholars have considered spatial correlation in their research Zhao et al. 2020;Zhong et al. 2022). Since the research subject of this study is carbon emissions among regions, it is possible that they may be closely related in terms of location. ...
Carbon reduction has become a major challenge for China’s economy in its transition toward sustainability. The government has been monitoring the behavior of enterprises through regulations to protect the environment, while green finance has rapidly developed in recent years as a new tool to reduce carbon emissions. Despite these measures, few studies have explored the interaction between these two drivers of carbon reduction. Therefore, this study aimed to examine the impact of green finance and environmental regulations on carbon emissions. To determine whether their coordination can lead to greater carbon reduction, the spatial spillover effect of this impact was also investigated. The results show that green finance can reduce carbon emissions and that the interaction of green finance with environmental regulations plays a significant positive role in reducing carbon emissions. Finally, this study concludes that the carbon reduction effects of green finance and environmental regulations have positive spillover effects on adjacent areas.
... By building a SAC model using the panel data of China's thirty provinces, Zhong et al. revealed a positive relationship between high-technology industry agglomeration and green total factor productivity. The study noticed that when industrial agglomeration develops to a relatively mature stage, industrial agglomeration can promote industrial ecological efficiency to improve economic benefits [3]. In addition, some scholars used the generalized linear regression model to consider the impacts of environmental regulation intensity on the development of green finance from the perspective of enterprises. ...
In this paper, an action mechanism of the carbon emission intensity (CE) reduction relevant to the level of industrial agglomeration (IA), scientific and technological progress (TECH), foreign direct investment (F), and environmental regulation (ER) is studied by applying a hybrid framework driven by the K-means clustering algorithm and the stepwise multiple linear regression (SMLR)-based models. To concisely clarify the relationship between the objected variable and impact factors, three echelons of urban agglomerations are summarized in this study from the different industry characteristics of the whole 21 cities in Guangdong, which exhibits a strong growth of the secondary industry in China. Stepwise multiple linear regression (SMLR) analysis is proposed to reveal that strict environmental regulations for the urban agglomeration with low overall development level and weak industrial foundation are more likely to stimulate the “innovation compensation” effect. For the urban agglomeration in the growth stage of industrial economic development or distributed in the developed region, strengthening the level of IA and highlighting the core guiding role of environmental regulation seems to make more sense to reduce the carbon emission intensity and promote the regional industrial green upgrading.
... First, industrial agglomeration promotes agricultural green production efficiency [8]; that is, industrial agglomerations have effects such as scale and knowledge spillover, which can continuously stimulate the endogenous momentum of agricultural green development by improving technological innovation, boosting regional competitiveness, optimizing resource allocation, and specializing the division of labor, among other things. Second, industrial agglomeration prevents increases in agricultural green production efficiency [9]. The congestion effect, which occurs when resource consumption exceeds the environment's capacity, is triggered by excessive industrial agglomeration. ...
Understanding how industrial agglomeration affects agricultural green production efficiency is essential for green agricultural development. This study uses the super-efficient Epsilon-Based Measure (EBM) model and Global Malmquist–Luenberger (GML) index to measure and analyze the spatial and temporal evolution characteristics and core sources of dynamics of agricultural green production efficiency in China by using panel data from 30 Chinese provinces from 2006 to 2020. It also empirically investigates the relationships between industrial agglomeration, land transfer, and agricultural production efficiency. By using fixed, intermediary, and threshold effect models, the internal links between industrial agglomeration, land transfer, and agricultural green production efficiency are examined. The findings indicate the following. (1) The green production efficiency of Chinese agriculture exhibits the regional characteristics of being “high in the west and low in the east, high in the south and low in the north” in terms of space; in terms of time, the overall trend is that green production technology efficiency is growing, with an average annual growth rate of 11.45%, and the growth primarily depends on the “single-track drive” of green technological progress. (2) Industrial agglomeration significantly affects agricultural green production efficiency, green technology efficiency, and green technology change; the corresponding coefficient values are 0.115, 0.093, and 0.022. (3) According to the mechanism-of-action results, land transfer mediates the effects of industrial agglomeration on agricultural green production efficiency, green technology efficiency, and green technology change. These effects have effect values of 28.48%, 27.91%, and 47.75%, respectively. (4) The threshold effect’s findings demonstrate a double threshold effect of industrial agglomeration on the green production efficiency of agriculture in terms of land transfer, with threshold values of 1.468 and 3.891, respectively. As a result, this study suggests adhering to the idea of synergistic development, promoting agricultural green development, strengthening the development of industrial agglomerations, promoting the quality and efficiency of industry, improving land-transfer mechanisms, and placing a focus on resource efficiency improvements, as well as other policy recommendations.
... Existing research has three primary opposing viewpoints regarding how industrial agglomeration affects agricultural green production efficiency: First, industrial agglomeration promotes agricultural green production efficiency [6]; that is, industrial agglomerations have effects such as scale and knowledge spillover, which can continuously stimulate the endogenous momentum of agricultural green development by improving technological innovation, boosting regional competitiveness, optimizing resource allocation, and specializing the division of labor, among other things. Second, industrial agglomeration prevents increases in agricultural green production efficiency [7]. The congestion effect, which occurs when resource consumption exceeds the environment's capacity, is triggered by excessive industrial agglomeration. ...
... Following the pertinent study by Hansen et al. [26], the threshold effect of land transfer was tested by using land transfer as a threshold variable to create a segmental function of industrial agglomeration with respect to agricultural green production efficiency. Equation (4) and the panel threshold effect model were configured as follows: 7 AGPE it =α+β 1 ...
... Control it +µ i +ε it (7) In Eq. (7), β 1 and β 2 are the coefficients to be estimated for the corresponding threshold intervals; I(·) is the indicative function, taking the value of 1 if the expression in parentheses is true and 0 otherwise; γ is the threshold value to be determined; the remaining variables have meanings consistent with those in Eq. (4). ...
Understanding how industrial agglomeration affects agricultural green production efficiency is essential for green agricultural development. This study uses the super-efficient EBM-GML to measure and analyze the spatial and temporal evolution characteristics and core sources of dynamics of agricultural green production efficiency in China by using panel data from 30 Chinese provinces from 2006 to 2020. It also empirically investigates the relationships between industrial agglomeration, land transfer, and agricultural production efficiency. By using fixed, intermediary, and threshold effects models, the internal links between the industrial agglomeration, land transfer, and agricultural green production efficiency are examined. The findings indicate that the green production efficiency of Chinese agriculture exhibits the regional characteristics of being “high in the west and low in the east, high in the south and low in the north” in terms of space; in terms of time series, the overall trend is that of growing, with an average annual growth rate of 11.45%, and the growth primarily depends on the “single-track drive” of green technological progress. By promoting land transfer, industrial agglomeration can increase the agricultural green production efficiency and decomposition index. Land transfer has a double-threshold effect on the influence of industrial agglomeration on agricultural green production efficiency. As a result, this study suggests adhering to the idea of synergistic development, promoting agricultural green development, strengthening the development of industrial agglomerations, promoting the quality and efficiency of industry, improving land-transfer mechanisms, and placing a focus on resource efficiency improvements, as well as other policy recommendations.
... The final category of literature has made the assertion that the nexus between IC and environmental pollutant emission issues is non-linear in different development stages from the diversity perspectives (18)(19)(20). For example, a typical non-linear shaped nexus between IC and green development (21), haze pollution (22), and industrial eco-efficiency (23) have been proposed in recent research. In addition, the multiform nonlinear relationship between IC and carbon emissions is also gradually being proposed and proved by scholars, such as inverted N-shaped nexus (24,25), an inverse U-curve nexus (8,26), a U-curve nexus (7,20), and so on. ...
Based on panel data of 285 cities in China at the prefecture level and above from 2005 to 2020, this paper aims to study the nexus between industrial co-agglomeration and carbon emissions from dual perspectives including space and time. It adopts multiple approaches including a dynamic general method of moment, panel quantile regression model, panel threshold model, and dynamic spatial Durbin model. The non-spatial empirical results support the establishment of the threshold effect and the imbalance effect. The spatial empirical results indicate that industrial co-agglomeration poses a dramatic stimulating effect on urban carbon emissions, and its spatial spillover effect and spatial heterogeneity are conditionally established. Furthermore, heterogeneous effects are supported, such as the positive spillover effects of industrial co-agglomeration are more significant in western cities, resource-oriented cities, and non-low-carbon pilot cities. The heterogeneous influence of cost factors on industrial agglomeration and carbon emissions has also been partially confirmed. In terms of the channels and mechanism of action, the negative externalities of industrial co-agglomeration occupy a dominant position in the current status of economic development. The dynamic equilibrium between government intervention and marketization is a solid foundation for the optimization of carbon emission reduction paths.
