Regional Low-carbon Development Research Articles

Study on stochastic convergence of China’s carbon emission efficiency

ABSTRACT Carbon emission efficiency (CEE) and its stochastic convergence are important indicators for achieving the strategic goal of carbon peaking and carbon neutrality. Thus, this paper firstly estimates the CEE of 30 provinces in China from 1995 to 2019 with the super efficiency of the SBM model. Then the panel unit root test method is adopted to test its stochastic convergence. The results show that the overall CEE has not reached the effective level, and the CEE level varies greatly among regions. There is a stochastic divergence of CEE in most regions from 1995 to 2001. While the CEE of most regions changes to convergence after 2002. From 1995 to 2019, the CEE of the western, northeast, and southwest regions diverge. While the CEE of the national level, the eastern, central regions, and the other six regions in the eight major economic zones converged stochastically. Among the divergent areas, the CEE of Heilongjiang, Guangxi, and Sichuan provinces are the main contributors to the CEE divergence. Therefore, it is the key to narrowing the differences in regional low-carbon development in China by significantly improving the CEE of each province.

Spatio-temporal Differentiation of County Carbon Budget and Territorial Space Optimization Zoning Strategy in Gansu Province from the Perspective of Carbon Neutrality

Under the background of comprehensively practicing the overall system concept of the "living community" in the new era, incorporating the carbon neutral development goal into the territorial spatial planning and construction and establishing the territorial spatial pattern and optimization strategy in line with the actual development of Gansu Province are of great significance for promoting the comprehensive green low-carbon transformation and high-quality development of regional economy and society. Taking counties in Gansu Province as an example, based on the perspective of carbon neutrality research, the land use carbon budget of 87 counties in Gansu Province in 2010, 2015, and 2021 was calculated and analyzed. GIS spatial analysis and social network analysis were used to further explore their spatial differentiation characteristics and the overall characteristics of the carbon emission spatial correlation network. At last, combined with the main function zoning, the low-carbon oriented land space optimization zoning was carried out, and differentiated low-carbon development strategies were proposed. The results were as follows： ① Carbon emissions in Gansu Province showed an upward trend, but the increase rate decreased, showing a spatial distribution of "high in the central and eastern part of the country, low in the southwest." Construction land was the main carbon source. The carbon uptake showed a spatial distribution of "high in the south and low in the north, high in the west and low in the east." Woodlands were the main carbon sinks. The net carbon emissions showed an increasing trend, and approximately 58.62% of the counties in the province were in a carbon imbalance situation. ② In 2021, the spatial network of county carbon emissions was closely related, showing a "core-edge" pattern. The Chenguan District and Qilihe District were in the core position of the network and received more correlation relationships in the network. The network contacts in Longzhong area were frequent, followed by the contacts in Longdongnan area. ③ Based on carbon emissions, carbon sequestration, and ecological carrying capacity coefficients and using the results of spatial correlation of social networks as role positions, the province was divided into four carbon-neutral sub-districts. At the same time, superimposed analysis of the main function zoning, the county area of the province was reconstructed into seven territorial space zones, and the differentiated regional low-carbon optimization development strategy was proposed for each zone.

Review of the Water–Land–Food–Carbon Nexus Focused on Regional Low-Carbon and High-Quality Agricultural Development

Review of the Water–Land–Food–Carbon Nexus Focused on Regional Low-Carbon and High-Quality Agricultural Development

Studying regional low-carbon development: A case study of Sichuan Province in China.

The unavoidable option for socially sustainable development is a low-carbon economy. One of the essential steps for China to attain high-quality development is reducing carbon emissions. It is necessary to realize low-carbon development in Sichuan, as it is not only an important economic zone but also an ecological protected area. The concurrent relationship among energy consumption, carbon emissions, and economic growth was examined in this study using the Tapio decoupling indicator, and the factors affecting energy consumption and carbon emissions in Sichuan were broken down using the logarithmic mean Divisia indicator (LMDI). The findings demonstrate a fundamental relative decoupling relationship between Sichuan's energy use and carbon emissions. Analysis of energy consumption and carbon emissions in Sichuan Province from 2005 to 2020 shows distinct patterns. From 2005 to 2012, in 2014, and from 2016 to 2020, the relationship between energy use and carbon emissions was relatively decoupled, with decoupling values ranging between 0 and 1. Absolute decoupling occurred in specific years: 2010, from 2013 to 2018, and in 2020. These periods are characterized by economic growth alongside reductions in carbon emissions. Factors affecting energy consumption and carbon emissions were consistently analyzed, showing similar impacts throughout the study periods. We find that population and economic growth are the main driving forces of these effects. The effects of energy intensity and industrial structure mainly play restraining roles, and the latter has a slightly weaker effect than the former.

Analysis of regional carbon productivity differences and influencing factors—based on new green decomposition model

This paper introduces a new green decomposition model of carbon productivity that aims to further analyze the regional differences in carbon productivity and its interaction with regional industrial performance. We combine desired outputs and undesired outputs orientation, and multiple factor inputs to derive a new green decomposition theorem, establish a new green decomposition model of carbon productivity, and obtain nine effects of regional carbon productivity differences. Empirical analysis is conducted using input-output data from 29 provinces and 15 industries in China, comparing the differences in carbon productivity from both the provincial and industry perspectives and exploring the mechanism of action. This paper provides theoretical basis and empirical evidence for regional carbon productivity enhancement and economic and industrial optimization from the perspective of multi-factor inputs, as well as policy insights for regional low-carbon transition development.

The impact of trans-provincial watershed eco-compensation policy on carbon emissions: Evidence from China

To manage transboundary pollution, China has promoted trans-provincial watershed eco-compensation (WEC), which is a market-oriented policy that adjusts ecological and economic interests between upstream and downstream areas and promotes regional green and low-carbon transformation. Hence, a reasonable evaluation of the carbon emission reduction effect of WEC is of great value for achieving green transformation. Using data from Chinese counties between 2003 and 2020, we consider 11 trans-provincial WEC agreements and evaluate their influence on carbon emissions. We find that trans-provincial WEC policies reduce water pollution emissions and significantly lower carbon emission levels. Mechanism tests show that the policy reduces carbon emissions by limiting polluting industrial development and stimulating low-carbon technological innovations. Moreover, this policy improves regional low-carbon economic development, and there are significant heterogeneity effects of the WEC in different regions and compensation modes. This research provides new insights for transitional countries to design green transformation policies.

Impact path of digital economy on carbon emission efficiency: Mediating effect based on technological innovation

The digital economy (DIE), a new economic form with digitalization at its core, has become an important driving force for promoting regional economy development. In the context of the COVID-19 pandemic, exploring the impact path of the DIE on carbon emission efficiency (CEE) is conducive to giving full play to the “carbon-reduction-and-efficiency-enhancement” role of the DIE, and to promoting the realization the “dual carbon” goal of carbon peak and carbon neutrality. In this paper, the Yellow River Basin (YRB) and the Yangtze River Economic Belt (YREB) are taken as study areas, the panel Tobit model is used to explore the impact of the DIE on CEE, and the intermediary-effect model and threshold-effect model are constructed to test the intermediary and threshold effects of technological innovation, respectively. The results show that the DIE has a U-shaped nonlinear impact on CEE in both the YRB and the YREB and that the impact has regional heterogeneity. Technological innovation can play a mediating effect between the DIE and CEE, whereas the mediating effect in the YRB is stronger than that in the YREB. Technological innovation has a threshold effect on the DIE to improve CEE, while the threshold value in the YREB is higher than that in the YRB. Furthermore, this paper proposes some suggestions to guide regional low-carbon and sustainable development.

Drivers and spatial patterns of carbon emissions from residential buildings: An empirical analysis of Fuzhou city (China)

Global economic development will continue to harm the natural environment. China is the largest contributor to global carbon emissions, with cities and the construction sector playing a major role. Understanding carbon emissions from buildings in cities can inform mitigation measures. This study conducted a comprehensive accounting of carbon emissions from lighting and appliance electricity consumption in residential buildings. The investigation covered Fuzhou city in Fujian Province of China in 2011–2021. ArcGIS generated and analyzed the spatial patterns of carbon emissions by districts. The STIRPAT model identified the leading carbon-emission drivers. The findings indicate: (1) fine-grained spatial distribution of carbon emissions demonstrates a marked concentration in the city center and contiguous districts, low emissions in surrounding districts, and a gradually increasing trajectory; and (2) GDP per capita, urbanization rate, and resident population are the principal drivers of carbon emissions, with every 1 % increase in GDP per capita raising carbon emissions by 0.66 %. Relevant practical energy-saving and emission-reduction measures are distilled from the results. The findings provide a scientific basis for decision-makers to formulate emission-reduction targets and strategies for Fuzhou's residential buildings, a theoretical basis for promoting regional low-carbon development, and a reference for other regions with a similar developing economy.

The influence and forecast of three industries and energy structure on regional carbon emission

ABSTRACT Carbon emission reduction is an important part of regional low-carbon economic development. In this paper, gray correlation analysis, neural network model, Gaussian multi-mode fitting and other methods were used to analyze the relationship between total carbon emissions and regional economic development, industrial structure, and energy consumption in Henan Province. On this basis, the future development of carbon emissions is predicted. The calculation results showed that the correlation between the three industries and carbon emissions in Henan Province is more than .7, among which the secondary industry has the highest correlation (.77). In the secondary industry, the correlation coefficient between coal and carbon emissions is the highest .87, while the correlation coefficient between other energy sources is about .5. In the neural network prediction model, the correlation coefficient between the prediction curve and the actual total carbon emission curve is .989, and the prediction results have a good degree of fit. The carbon emission prediction curve was divided into two parts: a linear decline stage from 2018 to 2024, and a rapid decline stage after 2024.The results showed that more efforts should be made in industrial structure, energy consumption structure and environmental protection to achieve low-carbon development in Henan province.

Mangrove Management Model Towards Net Zero Emissions in Sinjai Regency, South Sulawesi Province.

Mangrove ecosystem plays a very vital role in mitigating climate change, through carbon CO2 absorption. Mangroves have high potential as carbon sequestration. Sinjai Regency is one of the pilot project locations for district/city involvement in the Regional Low Carbon Development Plan in South Sulawesi Province, so that the policy continues to be implemented in various regions. Sinjai Regency has a Mangrove Area which is the result of rehabilitation, but there are indications of changes in land cover that are used conventionally and not integrated, causing a reduction in the area of mangrove land cover. This study aims to analyze the behavior of the model using three policy scenarios with the concept of environmental sustainability in terms of the contribution of mangrove forest utilization to emission absorption. The method used to see changes in land use from year to year is the spatial analysis method, then the data is analyzed using a dynamic system. This research was conducted from January to June 2022 in Sinjai Regency, South Sulawesi Province. The results of the analysis reveal that there are several types of land use that have decreased in area and some have increased in area from year to year. The simulation results show that the proportion of emissions is inversely proportional to changes in mangrove area. The recommended policy is to maintain a minimum land area in the Mangrove Area which is a Protected and Conservation Area and carry out mangrove rehabilitation. The policy structure in the mangrove sector is divided into three parts, scenarios under baseline, fair and ambitious conditions. The mangrove sector in baseline conditions can achieve net zero emissions in 2041, while fair conditions are achieved in 2027 and ambitious conditions are achieved in 2024.

Does energy resource misallocation affect energy utilization efficiency? Evidence from Chinese provincial panel data

Along with the rapid development of society, the position of energy has become more prominent in recent years. However, energy resource misallocation has been a common phenomenon, which has been a brake on energy efficiency promotion and regional low carbon development. Therefore, this study serves to investigate interprovincial energy resource misallocation in China across five typical industries from 2003 to 2017 by an improved Gini Coefficient method. Then a panel data model is built to explore whether energy resource misallocation has a significant impact on energy efficiency. Results show that the national energy resource misallocation index (MI), on average, fell from 0.461 in 2003 to 0.363 in 2017, presenting a decreasing trend, which means that China's interprovincial energy resource misallocation has been effectively alleviated. Furthermore, we find that the energy resource misallocation has a significant negative effect on interprovincial energy efficiency. More specifically, a 1% decrease in the energy resource MI causes a 0.268% increase in energy efficiency. In addition, the urbanization level, industrial structure, and the proportion of fossil energy could inhibit the improvement of energy efficiency, while the increase in economic level could exert a positive effect on the improvement of energy efficiency.

Spatio-temporal relationship between carbon emission and ecosystem service value under land use change: a case study of the Guanzhong Plain Urban Agglomeration, China

Changes in land use restructuring significantly impact carbon emissions and the provision of ecosystem service value (ESV). This study focuses on the Guanzhong Plain Urban Agglomeration, and accounts for carbon emissions and ecosystem service values caused by land use in the years 2000, 2005, 2010, 2015, and 2020. Carbon sources, carbon sinks, net carbon emission intensity, and ESV intensity were introduced as research variables and the spatial and temporal divergence and correlation patterns between them were examined. The results show that: 1) The carbon emission intensity of land use in the Guanzhong Plain Urban Agglomeration has increased significantly over the study period, showing a distribution pattern of high intensity in the centra regions and low intensity in the surrounding regions. Construction land was identified as the largest carbon source. 2) The overall ESV follows an increasing trend, with the total value increasing from 215,263.7 million to 216,776.2 million. The distribution of ESV intensity is low in the central regions and high in the surrounding regions, and significant changes were observed in the ESV loss and gain intensity of farmland and water body. 3) Carbon emissions and ESV show a significant negative spatial correlation, and both are dominated by low-high and high-low aggregation patterns. A spatial spillover effect of carbon emissions on ESV was observed. Through the correlation analysis of carbon emissions and ESV, theoretical support is provided for promoting regional low-carbon green development and eco-economic synergistic development.

Industrial carbon emission efficiency prediction and carbon emission reduction strategies based on multi-objective particle swarm optimization-backpropagation: A perspective from regional clustering

Against the backdrop of global climate change, industrial carbon emission reduction has become an important pathway to for global low-carbon development. This study constructs a framework of geographic spatial constraints regionalization and multi-objective machine learning to predict future industrial carbon emission efficiency (ICEE) and explore strategies for carbon emission reduction. Firstly, the ICEE of 285 Chinese cities were calculated by the super-efficiency slacks-based measure. Secondly, the cities were classified into four ICEE level regions through the spatially constrained multivariate clustering. Next, the multi-objective particle swarm optimization-BP (MOPSO-BP) model was constructed to predict the future trends of ICEE in the four regions. Finally, the geographical detector and multi-scale geographically weighted regression were employed for exploring driving force and carbon emission reduction strategies in different regions. The results show that most cities had low or medium ICEE, while super efficiency cities were mainly distributed in the east coastal areas. The prediction performance of the MOPSO-BP model for the four regions was better than the ordinary particle swarm optimization-BP and traditional BP model. Except for the Agricultural Production Region, there is considerable room for improving the ICEE of other regions over the next decade. Macroeconomic and microeconomic development have a global effect in promoting regional ICEE improvement, urban construction shows a promoting or inhibiting effect in different regions, and information technology has significant spatial heterogeneity in its influence within each region. The analysis framework developed in the study is a reliable solution for managing and planning ICEE and provides constructive suggestions for future regional low-carbon development.

Assessing the Impact of Spatiotemporal Evolution of Urbanization on Carbon Storage in the Mega-Urban Agglomeration Area: Case Study of Yangtze River Delta Urban Agglomeration, China

A comprehensive understanding of the relationship between urbanization evolution and carbon storage is crucial for regional low-carbon development and the mitigation of global warming. In this study, we took a typical mega-urban agglomeration (Yangtze River Delta region) in China from 2000 to 2020 as an example, introduced an improved urbanization index to evaluate its urbanization level, and analyzed the impact of urbanization on carbon storage. The results show that in the past 20 years, the urbanization level of the Yangtze River Delta has increased by 3.25 times, during which, carbon storage has always shown a downward trend and decreased by 6.56 × 107 t. Furthermore, there was a gradually increasing negative correlation between urbanization and carbon storage. Lastly, the spatial loss of carbon storage is as follows: urban–rural fringes > rural area > developed urban area. From the perspective of stage characteristics, urban development periods that focus on outward expansion suffer faster losses. The results point out that we should focus on urban–rural fringes and control the urbanization development model in order to achieve carbon storage protection in rapidly urbanizing areas. This study provides a unique perspective on how to coordinate the relationship between regional urbanization and carbon storage services and achieve sustainability, especially for mega-urban agglomeration regions.

Ecotourism certification and regional low-carbon sustainable development: A quasi-experimental study based on the Prototype-zone of National Ecotourism Attractions in China

Despite ecotourism certification's efficacy as a potential environmental management tool for promoting sustainable development, the applicability of Western ecotourism certification patterns in China and the environmental effects of China's ecotourism certification remain unclear. Based on China's Prototype-zone of National Ecotourism Attractions (PNEA) certification, this paper aims to provide empirical evidence on the causal relationship between ecotourism and low-carbon sustainable development in relation to public environmental concerns. The multiperiod Difference-in-Differences (DID) model is set up to examine the supervision of PNEA certification on regional carbon emissions based on the panel data from 285 Chinese prefecture-level cities in 2011–2017. Results demonstrate that PNEA approval can reduce regional carbon emissions by 1.1% per capita, and this effect would increase over time. Heterogeneity analysis shows that the carbon emission reduction impact is significantly higher on midwestern cities, resource-dependent cities, and cities with net population inflow. Furthermore, the determinants of public environmental concerns, such as PNEA's functions of tourism economic development and environmental education, are crucial for low-carbon sustainable development. The findings quite clearly account for the contributions of ecotourism certification in promoting sustainable development and the benefits of effective policy instruments.

Unleashing the potential: Exploring the nexus between low-carbon digital economy and regional economic-social development in China

In 2020, China formally proposed the goal of achieving carbon peaking in 2030 and achieving carbon neutrality in 2060. Since then, widespread attention has been devoted to determining when and how this goal will be achieved. This paper creatively puts forward the view of low-carbon digital economy from the perspective of resource flow, digital flow and energy flow, and studies the synergy effect of low-carbon digital development by constructing the synergy model of low-carbon digital development economic and social development. Based on theoretical research, this paper constructs a synergy model and evaluation index system between low-carbon digital development and economic and social development, and an empirical analysis was conducted based on data related to regional low-carbon digital development and economic and social development in China from 2014 to 2019. The empirical results show that the development of China's low-carbon digital economy is the main driving force for future economic and social development. The effects of low-carbon digital economy on economic and social development varied across regions due to their different regional development patterns. The spatial pattern shows a trend of “high in the east and low in the west”. Further, if the low carbon economy and the digital economy included in the stimulus policies can play a synergistic role, it will be able to play the greatest role in promoting economic and social development. And such a scenario is used as a practice in different regions of China in future. The research conclusion of this paper enriches the discussion on the construction of ecological civilization and the construction of a community with a common future for mankind.

Fiscal Expenditure on Sports and Regional Carbon Emissions: Evidence from China

Due to the problems of economic structure and the ways of industrial development, many countries have accumulated many ecological problems in the process of economic development, especially the increase in carbon emissions, the greenhouse effect, and the emergence of a series of problems, which makes the global ecosystem suffer severe challenges. Achieving green sustainable development has become a strategic development arrangement for all countries, and as the sports economy is closely linked to regional green development, it has become an important channel to influence regional green development by promoting sports development. Based on theoretical analysis, this paper empirically analyzes and tests the impact of government fiscal support for sports on regional carbon emissions and its mechanisms based on provincial panel data using baseline regressions, a mediated effects model, and a threshold model. We argue that the development of green and healthy sports can have a significant impact on regional green development and that government fiscal support for sports plays an important role in this process. We found that government fiscal support for sports can significantly reduce regional carbon emissions and thus promote regional green development. From the perspective of mechanism analysis, the government’s fiscal support for sports leads to the orderly development of the sports industry and its associated industries, which improves the rationalization of the regional industrial structure and thus reduces regional carbon emissions. Moreover, as the carbon productivity of the region continues to rise, the impact of fiscal support for sports on regional low-carbon development gradually deepens. This paper confirms the important role of the government’s emphasis on sports development in the process of achieving carbon peaking and carbon neutrality, verifies the mediating effect of industrial structure rationalization, and further quantifies the correlation through the threshold effect, extending the study of the influencing factors affecting regional carbon emissions.

Research on Regional low-carbon development path based on LEAP model: Taking the Lin-gang Special Area as an example

Based on the economic and social development, as well as the resource factor endowment in the Lin-gang Special Area, the LEAP model is employed as the research tool to analyze the energy consumption and carbon emissions from 2021 to 2060 under different scenarios. The results show that under the baseline scenario, the carbon emission in the special area basically enters the platform period after 2035; Under the low-carbon scenario, the carbon peak can be achieved in 2035, but the subsequent decline is relatively slow; Under the enhanced low-carbon scenario, the special area can reach the peak synchronously with the national goal in 2030 and basically achieve carbon neutrality in 2060. In addition, the contribution analysis of emission reduction for different policy measures shows that the contribution of clean energy substitution is the largest, followed by energy efficiency improvement and electrification.

Research on the impacts of dual environmental regulation on regional carbon emissions under the goal of carbon neutrality-the intermediary role of green technology innovation

As an important means to promote regional low-carbon development, environmental regulation has great theoretical and practical significance for achieving the goal of carbon-neutral development in China. Based on the panel data of 30 provinces and cities in China from 2005 to 2019, this paper first uses the intermediary effect model to analyze the impact of the implementation of environmental regulation policies on regional carbon emissions, discusses the relationship between environmental regulation, green technology innovation and carbon emissions, and further uses the threshold effect model to discuss the nonlinear relationship between environmental regulation and regional carbon emissions. The results show that: the improvement of formal and informal environmental regulation can play a role in “forced emission reduction”; Green technology innovation plays a complete intermediary role in the impact of environmental regulation on regional carbon emissions; At the same time, the impact of formal and informal environmental regulation on regional carbon emissions has a threshold effect. Among them, formal environmental regulation has a double threshold effect, the threshold values are 0.429 and 0.502 respectively, while informal environmental regulation has a single threshold effect, the threshold value is 1.803. The results of heterogeneity analysis show that there are obvious differences in the implementation effects of environmental regulation policies under different economic development levels and industrialization development stages. Therefore, we should pay attention to the mutual promotion effect of different types of environmental regulation, strengthen the collaborative emission reduction of environmental regulation and green technology innovation, and improve the level of regional green technology innovation, so as to better promote the realization of regional carbon neutrality goals.

A New Machine Learning Algorithm for Regional Low-Carbon Economic Development Analysis Based on Data Mining

The development of information technology such as the continuous improvement of mobile Internet infrastructure and the performance of computers has made it easy to process and share information. The huge market demand for location-based information services provides huge impetus to the generation and development of mobile terminal positioning technology. Generally speaking, the main causes of climate change can be summarized into two categories: natural climate fluctuations and the impact of human activities which is a major measure taken by China to actively respond to climate change. This is a successful approach to actively explore the rapid development of China’s industrialization and urbanization, which not only develops the economy and improves people’s livelihood but also responds to climate change and reduces carbon intensity. Firstly, this paper mainly is aimed at the connotation of regional low-carbon economic development mode, studying the basic mode of regional low-carbon economic development, and analyzing the characteristics and applicable conditions of each mode. Secondly, based on the machine learning algorithm of data mining, the main mode selection of regional low-carbon economic development is discussed. Thirdly and finally, when choosing the regional low-carbon economic development mode, comprehensive consideration should be given to the economic development basis, energy structure, resource characteristics, industrial status, development mode, geographic location, and other factors. This paper studies the basic conditions and applicable conditions of regional economic development models. The conclusion shows that from the perspective of regional economic evolution, low-carbon economy can be regarded as the decarbonization process of economic development. It is an economic form combining its own characteristics and an inevitable requirement for the transformation of regional economy from other economic models to low-carbon economic models. And other factors of Selection of regional economic development foundation, energy structure, resource characteristics, industrial status, development mode, geographical location, were also discussed.