Inequalities of China's regional low-carbon development

Coping with the tale of natural resources and environmental inequality: an application of the machine learning tools

With the rising momentum according to the environmentalist voices seeking climate justice for more equity and the importance of encouraging environmental justice mechanisms and tools, in this perspective, the objective of this study is to analyze in depth the substantial role of natural resources abundance in the environmental inequality issue. For this purpose, this study adopted the eXtreme Gradient Boosting (XGBoost), LightGBM, Natural Gradient Boosting (NGBoost), Hybrid hybrid upper confidence bound-long short-term memory-Genetic Algorithm (UCB-LSTM-GA), and the Shapley Additive Explanation (SAE) machine learning algorithms in the context of 21 emerging economies spanning the years 2001 to 2019. The empirical results reveal that natural resource abundance, foreign trade, and foreign direct investment inflows contribute all to higher levels of environmental inequality. However, higher levels of per capita income, gross fixed capital formation, and institutional quality contribute to lower levels of environmental inequality. Addressing climate justice holistically through an integrated supranational vision is significant since every step taken toward eradicating environmental racism matters.

Assessing financial inclusion co-movement with low-carbon development index: implications for regional development

This study assesses the relationship between financial inclusion and low-carbon development and its implications for regional development. Data envelopment analysis (DEA) is used to analyse data from a sample of E7 countries from 2001 to 2020. Results show that financial inclusion and low-carbon development are significantly related, indicating that financial inclusion is a significant driver of low-carbon development. Additionally, regional differences between E7 countries in financial inclusion and low-carbon development are identified, highlighting the need for region-specific policies to promote sustainable development. Moreover, findings show that deposits, bank accounts, and ATMs of all E7 economies contribute to environmental and economic growth inputs. The findings have important implications. Therefore, encouraging low-carbon growth via increased financial inclusion may be successful. Access to financial services, financial literacy, and financial innovation are all areas where policymakers might do more to advance the cause of financial inclusion. Accounting for variations in financial inclusion and low-carbon development between regions is important when crafting policies to encourage sustainable development. The economic climate, cultural norms, and institutional structures of a given area must all be considered by policymakers if they are to craft plans that will be successful.

Renewable energy technology innovation and inclusive low-carbon development from the perspective of spatiotemporal consistency

As an emerging driving factor, the positive impact of renewable energy technology innovation (RETI) on inclusive low-carbon development (ILCD) may be undervalued or even neglected. This paper develops an evaluation system to measure China's ILCD by using provincial panel data from 2006 to 2020. Based on the combined perspective of spatial spillover effect and threshold effect, this paper examines the spatial spillover effects and the regional boundary of RETI on ILCD in different periods and further analyzes five heterogeneities. The results show that (1) RETI and ILCD are increasing steadily, presenting a spatial pattern of "high in the east and low in the west." (2) Overall, RETI significantly promotes ILCD in local and neighboring areas. RETI in the growth period inhibits local ILCD, which in the mature period promotes local and neighboring ILCD. (3) The spatial spillover boundary of the whole RETI is 1400 km, that of RETI in the growth period is 1000 km, and that of RETI in the mature period is 1600 km. (4) The promotion effect of RETI on ILCD enhances over time and shows a spatial pattern of "eastern > central > south > north > western." It is further found that RETI strongly promotes ILCD in non-resource-based, high marketization, and strong environmental regulation areas. Therefore, it is necessary to break down administrative and market barriers, strengthen inter-regional cooperation and interconnection of resource elements, and establish a dynamic management mechanism of "one province, one policy" according to the regional heterogeneity for providing decision-making reference in promoting global energy transition and climate governance.

Spatial-temporal differentiation and dynamic spatial convergence of inclusive low-carbon development: evidence from China

Promoting green and low-carbon development has become a global consensus. However, few studies have explored the scientific connotation and influencing factors of inclusive low-carbon development (ILCD). Based on the perspective of inclusive growth and sustainable development, this paper constructs an economic growth-social inclusion-low carbon development (ESC) analytical framework and uses the improved entropy weight method to estimate the ILCD level of 30 provinces in China from 2006 to 2020. Kernel density estimation, Moran's I index, and Dagum Gini coefficient decomposition are used to characterize the spatial and temporal variation patterns, and the variation coefficient and the dynamic spatial Durbin model are used to explore the dynamic spatial convergence characteristics. The results show that (1) ILCD in China is increasing steadily overall, but there is a significant spatial non-equilibrium characteristic of high in the east and low in the west. (2) The overall difference in ILCD shows a trend of fluctuation and narrowing, and the imbalance between regions is the primary source of the overall difference. (3) ILCD has significant path dependence and dynamic spatial convergence characteristics in the whole country and the three regions, and the spatial spillover effect tends to be good neighborliness. The rate of convergence shows a trend of national (4.77%) > western (2.33%) > eastern (1.97%) > central (0.96%), and the spatial convergence of the club is gradually becoming prominent. (4) The factors including economic development, technological innovation, urbanization level and human capital significantly affect ILCD, which also has regional heterogeneous. Therefore, it is necessary to break down administrative and market barriers, strengthen cross-regional cooperation and resource interconnectivity, and bring into play policy synergy to provide decision-making reference for achieving inclusive low-carbon development.

Spatial-Temporal Evolution and Driving Factors of the Coupling Coordination between Urbanization and Urban Resilience: A Case Study of the 167 Counties in Hebei Province

Urban resilience, as an important ability to deal with disasters in the process of urbanization, has been paid more and more attention as the result of the increasing risks that are caused by rapid urbanization. China is taking the county level as the basic unit to promote new-type urbanization and constructing resilient cities has become one of the development strategies. However, to achieve this strategy researchers need to analyze the interaction between county urbanization and urban resilience and its driving mechanism, which have been paid little attention. Therefore, this paper selected 167 counties in Hebei Province as the investigation subject. Based on the statistical data from 2010 to 2020, a comprehensive index system was developed to quantify the degree of coupling coordination between urbanization and urban resilience, and the spatial Durbin model was used to analyze the driving mechanism of it. The study shows that: Firstly, the urbanization level of counties rose year after year, with there being a geographical distribution that was "lower from southeast to northwest". The level of urban resilience increased year after year, showing a geographical distribution that was "higher from south to north" and a "core-edge" feature that was localized. Secondly, the coupling coordination degree increased steadily, and the overall level changed from a basic imbalance to a mild imbalance. In space, it is bounded by "Pingquan City-Pingshan County", which showed the distribution of "high in the east and low in the west, high in the center and low on the outskirts". Thirdly, the coupling coordination degree has spatial spillover effect. Government financial expenditure, innovation level, industrial upgrading level and urban shape index all influence the coupling coordination degree positively, with a successively decreasing impact, while the urban compactness has significant negative impacts. This study indicates that the regional differences exist in the coupling coordination degree, and the counties in different development stages need to adopt different strategies to promote the coordinated development of urbanized and resilient cities. Inter-regional support is also necessary in this process. Meanwhile, it is necessary for the government to govern various urban elements, especially in terms of their urban form.

Environmental regulation, R&D investment, and green technology innovation in China: Based on the PVAR model

The unreasonable economic development model of human beings has caused the environmental pollution problem to become increasingly serious. In order to achieve a positive relationship and interaction between environmental regulation, research and development (R&D) investment, and green technology innovation, and effectively solve the "strange circle" problem between high-quality economic development and environmental pollution in China and even the world, this paper takes the panel data of industrial enterprises above designated size in Chinese mainland 31 provinces from 2009 to 2019 as a research sample. The comprehensive index of R&D investment and green technology innovation was established by the entropy method, and the panel vector autoregressive (PVAR) model was constructed from the dynamic endogenous perspective, and the dynamic interaction and regional heterogeneity between environmental regulation, R&D investment, and green technology innovation were empirically analyzed by using impulse response function and variance decomposition. We obtain the following findings: (1) Environmental regulation has a two-way interaction relationship with R&D investment and green technology innovation, and R&D investment has a promotion effect on the "green degree" of technological innovation, but its role is still weak and has lagging characteristics. (2) There is significant regional heterogeneity in the dynamic responses of the eastern, central and western parts of China. (3) In the long run, environmental regulation has a "negative crowding out effect" on R&D investment in the central region, and the phenomenon of "central collapse" still exists but will gradually weaken. Environmental regulation has a "positive innovation compensation effect" on green technology innovation. Green technology innovation and R&D investment have an obvious "Pareto improvement" effect on environmental regulation, especially in the eastern region. The conclusions of this study help to clarify the dynamic interaction between environmental regulation, R&D investment, and green technology innovation, further improve environmental regulatory policies and green technology innovation R&D decision-making, and provide an effective way to achieve green and sustainable development in China and other parts of the world.

Does environmental regulation affect the export competitiveness of Chinese firms?

Strict environmental governance urges firms to implement higher sustainable production requirements and environmental standards for export products, making it challenging for firms to maintain export competitiveness. This study constructs firm-product level markups as the measure of export competitiveness using microdata of millions of observations from Chinese industrial firms. It then empirically investigates the impact of environmental regulations on those firms' export competitiveness. The empirical study finds: 1) environmental regulations have significantly increased the markups of Chinese firms' export products, indicating that firms' export competitiveness is enhanced, verifying the validity of the Porter Hypothesis. 2) Environmental regulations have a greater effect on increasing the markups of non-core products than that of core products. 3) Environmental regulations have significantly increased the markups for private-owned firms, foreign-invested firms, and firms in the eastern region, but not for state-owned and non-eastern firms. 4) Environmental regulation mainly enhances firms' export competitiveness mainly through the TFP channel. Moreover, a series of robustness tests support the empirical results. This research recognizes the positive achievements and benefits of China's environmental governance. It recommends that the Chinese government strengthen the supervision of state-owned enterprises and help enterprises in economically underdeveloped areas overcome the challenges brought by environmental regulation.

Tracking the inequalities of global per capita carbon emissions from perspectives of technological and economic gaps

Facing up to global carbon inequality is an important prerequisite for the fair allocation of carbon emission reduction tasks. This study proposed a new index to measure carbon inequality and then constructed a data envelopment analysis-based decomposition framework to focus on the impacts of the economic development gap and the technology gap on carbon inequality. Based on a dataset of 83 countries (or regions) during 1990-2017, we discuss global carbon inequality and its driving factors and further reveal the discrepancies among different income groups and countries. The main findings indicate that: (1) global carbon emissions showed significant inequality in all years and have an overall decreasing tendency. (2) High-income countries tended to display larger carbon inequality and witnessed great variation in different years. Upper middle-income and lower middle-income countries had relatively low carbon inequality and only experienced very minor variation. (3) Economic development disparity was the foremost factor leading to carbon inequality enlargement in most countries, especially the countries with higher income. The production technology gap played an important role in narrowing carbon inequality, whereas the energy-saving technology gap only exerted a minor effect on carbon inequality.

Dynamic Evolution and Regional Disparity in Carbon Emission Intensity in China

China’s carbon reductions are of great significance to the realization of global temperature control targets. Carbon emission intensity (CEI) represents the degree of coordination between emissions and economic development to some extent. Nevertheless, there is a paucity of research on its spatial–temporal evolution and regional differences. To fill the gap, this study exploits the Theil index to shed light on the characteristics of its spatial–temporal distribution and regional disparities in China during the period of 2000–2019, and constructs a multi-regional spatial index decomposition model to analyze the differences in its drivers. The results indicate that the decreasing CEI during the period of 2000–2019 shows a distinctive imbalance in spatial–temporal distribution. The gap between north and south is greater than that between east and west. The expansion of the Theil index based on CEI reveals a widening tendency of the mismatch between emissions and economic development among provinces. CEI disparity is mainly due to growing intraregional differences. For most provinces, the energy intensity effect is the essential driver of spatial differences regarding CEI, with the energy structure and the industrial structure effects gradually changing from promoting to inhibiting effects. The carbon emission factor effect has no significant fluctuation, but regional differences are distinct.

Environmental Protection, Industrial Structure and Urbanization: Spatiotemporal Evidence from Beijing–Tianjin–Hebei, China

The Beijing–Tianjin–Hebei urban agglomeration (BTH) is striving to realize the transformation process from a low-efficiency to a high-quality development mode; however, it still has problems regarding reducing energy consumption and ecological environment pressure. Based on panel data from 2013 to 2017, this paper proposes an evaluation index system based on BTH’s “environmental protection–industrial structure–urbanization” system. In the course of applying the coupling degree model (CDM) and the coupling coordination degree model (CCDM) with exploratory spatial data analysis (ESDA) methods, this paper discusses the spatiotemporal process, development level, and spatial agglomeration characteristics of the environmental protection–industrial structure–urbanization system in each city of the BTH area. The findings reveal that the coupling degree of the BTH system is gradually increasing, and that the development level of the BTH subsystem is unbalanced: the coupling coordination level of BTH shows a positive evolution process; however, it is in a stage of low-level collaborative development, and there are obvious differences in the level of BTH coupling coordination in space, revealing the convergence of low–high and high–low types. This paper concludes by putting forward the strategy of optimizing the regional spatial pattern of urban agglomeration and implementing integrated development in order to achieve the desired coupling and coordination effects.

Downscaling Building Energy Consumption Carbon Emissions by Machine Learning

The rapid rate of urbanization is causing increasing annual urban energy usage, drastic energy shortages, and pollution. Building operational energy consumption carbon emissions (BECCE) account for a substantial proportion of greenhouse gas emissions, crucially influencing global warming and the sustainability of urban socioeconomic development. As a foundation of building energy conservation, determination of refined statistics of BECCE is attracting increasing attention. However, reliable and accurate representation of BECCE remains lacking. This study proposed an innovative downscaling method to generate a gridded BECCE intensity benchmark dataset with 1 km2 spatial resolution. First, we calculated BECCE at the provincial level by energy balance table application. Second, on the basis of building climate demarcation, partial least squares regression models were used to establish the BECCE behavior equations for three climate regions. Third, Cubist regression models were built, retrieving down scale at the prefecture level to 1 km2 BECCE, which well-captured the complex relationships between BECCE and multisource covariates (i.e., gross domestic product, population, ground surface temperature, heating degree days, and cooling degree days). The downscaled product was verified using anthropogenic heat flux mapping at the same resolution. In comparison with other published pixel-based datasets of building energy usage, the gridded BECCE intensity map produced in this study showed good agreement and high spatial heterogeneity. This new BECCE intensity dataset could serve as a fundamental database for studies on building energy conservation and forecast carbon emissions, and could support decision makers in developing strategies for realizing the CO2 emission peak and carbon neutralization.

Carbon Productivity and Mitigation: Evidence from Industrial Development and Urbanization in the Central and Western Regions of China

This paper investigates carbon productivity (CP) from the perspectives of industrial development and urbanization to mitigate carbon emissions. We propose a hybrid model that includes a spatial lag model (SLM) and a fixed regional panel model using data from the 17 provinces in the central and western regions of China from 2000 to 2018. The results show that the slowly increasing CP has significant spatial spillover effects, with High–High (H–H) and Low–Low (L–L) spatial distributions in the central and western regions of China. In addition, industrial development and urbanization in the study area play different roles in CP, while economic urbanization and industrial fixed investment negatively affect CP, and population urbanization affects CP along a U-shape curve. Importantly, the results show that the patterns of industrial development and urbanization that influence CP are homogenous and mutually imitated in the 17 studied provinces. Furthermore, disparities in CP between regions are due to industrial workforce allocation (TL), but TL has been inefficient; industrial structure upgrades are slowly improving conditions. Therefore, the findings suggest that, in the short term, policymakers in China should implement industrial development policies that reduce carbon emissions in the western and central regions by focusing on improving industrial workforce allocation.

Mapping the knowledge domain of the evolution of emergy theory: a bibliometric approach

Bibliometric and visualization tools are effective for uncovering information contained in vast amounts of literature data. We retrieved publications from the Web of Science (2008-2020) and used the Statistical Analysis Toolkit for Informetrics (SATI) and CiteSpace to conduct a bibliometric analysis of emergy studies, review the focus of the studies, summarize current research, and identify academic trends. The SATI-based charts provided an overall description of yearly published articles, literature sources, high-frequency keywords, authors, institutions, and highly cited papers. CiteSpace was applied to determine the frontiers and hotspots of emergy theory. A dual-map analysis was used to reveal the connection between multiple disciplines. Keyword clustering analysis was performed to evaluate research hotspots. Moreover, burst analysis and timeline maps presented a holistic picture of cutting-edge issues and the evolutionary trajectory of emergy studies in recent years. An alluvial flow map analysis of the landmark literature depicted the transmission of knowledge in the emergy field. In addition, this article also evaluated the current status of collaborative research in emergy-related fields. The results can provide a reference for researchers to explore hot issues or target a specific field of study.

What drives the decoupling between economic growth and energy-related CO2 emissions in China's agricultural sector?

Many studies have shown that the rapid agricultural mechanization development in China led to substantial energy consumption and CO₂ emission growth. To better explain the mechanism behind the decoupling between economic growth and CO₂ emissions, this paper extends the logarithmic mean Divisia index (LMDI) and production-theoretical decomposition (PDA) considering agricultural decoupling from both structural and technical perspectives. The results reveal that (1) China's agricultural decoupling performance was not ideal. Investment and investment efficiency were the most important factors influencing the decoupling status. The main decoupling obstacle was a higher investment in productivity rather than in energy conservation and carbon reduction. (2) The decoupling status and investment orientation of decoupling efforts among regions were different. Strong negative decoupling statuses frequently occurred in the eastern region, whose main disadvantage was high potential energy intensity. The decoupling status of the central region exhibited expansive features. The decoupling key is to invest more in energy-saving technology rather than in production. The western region changed from weak decoupling to expansive negative decoupling. Both output technology and energy-related factors should be the main investment targets. (3) Weak decoupling and expansive negative decoupling were the most common statuses among provinces. The influence mechanism of drivers exhibited a high spatial heterogeneity at the provincial level. Therefore, the study offered a convincing basis for local governments to formulate low-carbon agricultural development policies by identifying the main decoupling drivers.

Nonlinear influence of urbanization on China's urban residential building carbon emissions: New evidence from panel threshold model

Carbon mitigation in the urban residential building sector is critical for China to achieve its carbon peak and carbon neutral commitment. However, how urbanization affects urban residential building carbon emissions is still unclear. This study adopts the panel threshold regression model to explore the dynamic influence mechanism of the urbanization on urban residential building CO₂ emissions based on the evidence from China's 30 provincial regions during 2000-2015. Results indicate that urbanization contributes positively to the increase of urban residential building CO₂ emissions, while such degree of influence varies across different stages of income and energy structure. As for per capita income, the promoting effect of the urbanization on urban residential building CO₂ emissions is enhanced with the growth of per capita income. And the degree of such increasing effect becomes greater when per capita income exceeds its threshold value. Regarding the energy mix, the driving effect of urbanization on urban residential building CO₂ emissions is also strengthened when the energy mix crosses its threshold value, showing a "stepwise growth" feature. This study reveals the nonlinear influence mechanism of urbanization on urban building CO₂ emissions, and this is helpful in boosting the related theoretical and practical exploration on the impact of urbanization on the environment. Based on our findings, an environmentally-friendly consumption pattern should be promoted and more penetration of cleaner energies should be improved in urban households, which will be effective to alleviate the increase of residential carbon emissions.

Exploring the impact of transition in energy mix on the CO2 emissions from China's power generation sector based on IDA and SDA

The energy transition from coal and oil to renewable energy, nuclear energy, and natural gas is a fundamental way for emission reduction of China's power generation sector. Until now, research on the drivers of CO₂ emissions from China's power generation sector has generally evaluated the energy mix as a whole, with a lack of exploration of the decomposition of different types of energy. This paper uses both index decomposition analysis (IDA) and structural decomposition analysis (SDA) to explore the impacts of energy transition on CO₂ emissions in the power generation sector during periods of 2002-2007, 2007-2012, and 2012-2017. We find that the results of IDA and SDA are almost consistent, indicating that our results are robust. During the whole study period, CO₂ emissions of power generation sector increased by 2447 Mt, of which the fossil fuel structure significantly contributed 642 Mt of incremental emissions (IDA). The thermal power generation efficiency was a dominator for reducing emissions, with a total reduction of 586 Mt (IDA). Simultaneously, the impacts of renewable energy and nuclear energy on emission reduction tend to be strengthening over time, with values changing from 38 Mt and -5 Mt in 2002-2007 to -219 Mt and -83 Mt (IDA) in 2012-2017, respectively. Based on the results, we put forward some suggestions such as promoting coal-to-gas, renewable energy, and nuclear energy in power generation to cut down CO₂ emissions of China's power generation sector.