Environmental regulation, Foreign investment behavior, and carbon emissions for 30 provinces in China

Currently, China is moving towards the era of the digital economy, which is gradually becoming a new engine of high-quality development. In the "double carbon" strategy context, the digital economy is characterized by low carbon emissions and high permeability, making it essential for carbon emission reduction. There needs to be more empirical research on the digital economy and carbon emissions. Given this, this study empirically examines the impact of digital economy development on carbon emissions intensity and its mechanisms in a multidimensional way based on the panel data of 30 provincial-level administrative regions in China from 2011 to 2019, utilizing a fixed-effects model, a mediated-effects model, a spatial Durbin model, and other methods. The study results show that (1) the digital economy can significantly reduce carbon emissions intensity. (2) The digital economy can indirectly affect the intensity of carbon emissions through industrial structure, energy structure, and environmental regulation. (3) The development of the local digital economy has a positive spillover effect on the carbon emissions intensity of neighbouring places. However, the overall effect is negative. This paper reveals some new features of the digital economy and carbon emissions intensity, which provides a reference for advancing the country's construction and realizing China's "double carbon" goal.

As a major carbon emitter, the electricity sector is crucial to the realization of China's emission reduction objectives. Existing studies focus mostly on the influencing factors, emission efficiency and low carbon development of carbon emissions in the electricity sector. Missing from the literature is an analysis of spatial characteristics of carbon emissions and the embodied carbon emission transfer caused by the separation of electricity production and consumption, which is the basis for assigning the responsibility for emission reduction. Thirty provinces in China were taken as research objects, and Moran's I index was adopted to analyze the spatial characteristics of the electricity sector's carbon emissions and carbon emission intensity. Based on multiregional input-output tables, we compared the transfer situation of China's provincial electricity carbon emissions in 2010 and 2015. The results demonstrate that, from 2010 to 2015, the electricity carbon emissions in 20 provinces increased, whereas the carbon emission intensity in 21 provinces decreased. Carbon emissions and carbon emission intensity of electricity in most provinces demonstrate positive spatial clustering characteristics. The total amount of carbon emission transfer in the electricity sector increased from 421.22 million tons in 2010 to 581.369 million tons in 2015, the number of net transfers out of areas increased from 13 to 15, and the number of net transfers into areas decreased from 16 to 15. The active degree of carbon emission transfer reveals the eastern region > the central region > the western region. Different emission reduction policies should be formulated based on the difference in resource endowment between the north and south. Provinces that transferred out large amounts of electricity carbon emissions should take greater responsibility for emission reduction. Integr Environ Assess Manag 2022;18:258-273. © 2021 SETAC.

Foreign direct investment (FDI) plays an important role in promoting industrial structure and curbing carbon emission. The study is based on panel data from 30 provinces in China from 2011 to 2021 and verifies the impact of FDI under environmental regulation on industrial structure upgrading and carbon emission. The empirical results show that FDI under environmental regulation can inhibit carbon emission and promote industrial structure upgrading. The carbon emission reduction effect and industrial structure upgrading effect of FDI show regional heterogeneity, with the strongest effect in the eastern region, followed by the central region, and no significant effect in the western region. The moderating effect examination of environmental regulation illustrates that formal and informal environmental regulation can effectively regulate the relationship between FDI and carbon emission, but due to differences in various factors such as economic development level and population quality, the moderating effect also exhibits regional heterogeneity. In the mechanism test, industrial structure upgrading plays a perfect mediating role in the path of FDI inhibiting carbon emission, and environmental regulation can further enhance the mediating effect of industrial structure upgrading. There is a threshold of industrial structure upgrading between FDI and carbon emission, and FDI can only suppress carbon emission after crossing the threshold of industrial structure upgrading.

Urban production energy consumption produces a large amount of carbon emissions, which is an important source of global warming. This study measures the quantity and intensity of carbon emissions in 30 provinces of China based on urban production energy consumption from 2005–2019, and uses the Dagum Gini coefficient, kernel density estimation, carbon emission classification and spatial econometric model to analyze the spatial and temporal distribution and driving factors of quantity and intensity of carbon emissions from China and regional production energy consumption. It was found that the growth rate of carbon emission quantity and carbon emission intensity of production energy consumption decreased year by year in each province during the study period. The imbalance of carbon emission was strong, with different degrees of increase and decrease, and there were big differences between eastern and western regions. The classification of carbon emissions differed among provinces and there was heterogeneity among regions. The quantity and intensity of carbon emissions of production energy consumption qwre affected by multiple factors, such as industrial structure. This study provides an in-depth comparison of the spatial and temporal distribution and driving factors of quantity and intensity of carbon emissions of production energy consumption across the country and regions, and provides targeted policies for carbon emission reduction across the country and regions, so as to help achieve China’s “double carbon” target quickly and effectively.

Achieving carbon emission reduction synergy is vital for green economic transformation. This study examines whether environmental governance pressure promotes such synergy, simultaneously driving carbon reduction and pollution control. Leveraging the 2012 Ambient Air Quality Standard as a quasi-natural experiment, we employ a continuous difference-in-differences (DID) method on 250 prefecture-level cities from 2009 to 2022. Our findings reveal that increased environmental governance pressure significantly reduces both the total amount and intensity of carbon emissions, demonstrating a clear synergistic effect. This synergy is positively correlated with reductions in major air pollutants (e.g., SO2 and NOx), indicating that pressure curbs both the total amount and intensity of carbon emissions. Mechanistic analysis shows that this pressure primarily curtails carbon emissions by fostering green innovation and accelerating cleaner energy transitions, with no ‘green paradox’. It also promotes low-carbon industrial restructuring while reducing reliance on end-of-pipe pollution management. Heterogeneity analysis indicates stronger synergistic effects in regions with lower emission reduction costs (e.g., western China, less developed industrial bases). We recommend robust central government environmental regulation policies to amplify local governance pressure, strengthen carbon reduction synergy, and facilitate continuous green development.

Despite achieving remarkable development, China’s agricultural economy has been under severe environmental pressure. Based on previous studies, the present study further considers the sources of agricultural carbon emissions in depth, estimates China’s agricultural carbon emissions from 1997 to 2016, and analyzes the agricultural pollution faced by China and its provinces. The study estimates the amount and intensity of agricultural carbon emissions in China from five carbon sources—agricultural materials, rice planting, soil N2O, livestock and poultry farming, and straw burning—and analyzes their spatial and temporal characteristics. The following results were obtained: (1) between 1997 and 2016, the amount of agricultural carbon emissions in China generally increased, while the intensity of agricultural carbon emissions decreased; (2) in the same period, the amount of carbon emissions from each category of carbon source generally increased, with the exception of rice planting; however, the amount of emissions fluctuated; (3) the amount and intensity of carbon emissions varied greatly among provinces; (4) the emissions from different categories of carbon source showed different concentration trends and agglomeration forms; (5) China’s agricultural carbon emissions showed obvious spatial correlation, which overall was high–high agglomeration; however, its carbon emissions gradually weakened, and the spatial agglomeration of agricultural carbon emissions in each province changed between 1997 and 2016.

Does the transformation of resource-dependent cities promote the realization of the carbon-peaking goal? An analysis based on typical resource-dependent city clusters in China

Exploring the path and mechanism of marketization level in the effect of foreign direct investment (FDI) on carbon emission performance will help to maximize the stimulation effect of foreign investment on green and low-carbon development. This study used the panel data of 11 provinces and cities in the Yangtze River Economic Belt from 2008 to 2016. A panel threshold model is constructed to explore the non-linear relationship between FDI and carbon emissions performance from the perspective of marketization level. The main conclusions are as follows: First, from the perspective of marketization level, a significant double threshold effect exists between foreign participation and carbon emission intensity, with threshold values of 4.4701 and 9.2516 respectively. Second, as the marketization level increases, the technology spillover effect of FDI increases, and the stimulation effect of foreign participation on carbon intensity decreases significantly, but it does not inhibit carbon intensity, indicating that the overall benefits brought by FDI technology spillovers are still insufficient to offset pollution caused by foreign investment. Third, the eastern region of the Yangtze River Economic Belt has crossed the second threshold. In the central and western regions, the marketization process is relatively slow except for Chongqing, and the regions are still firmly stuck between the first and second thresholds. In response to the conclusions of the empirical research, relevant policy suggestions are put forward from three dimensions, namely, the strategy of introducing foreign investment, construction of the marketization system, and environmental regulation, to achieve low-carbon and green development in the Yangtze River Economic Belt.

Based on the panel data of 30 provinces (municipalities and autonomous regions) in China from 2005 to 2019, this paper uses Gini coefficient decomposition and kernel density estimation to investigate the regional differences and dynamic evolution trend of rural energy carbon emission intensity in China. Then, the convergence model is used to analyze the convergence characteristics and influencing factors of carbon emission intensity. The study found the following: (1) During the observation period, the carbon emissions of coal energy and oil energy were much higher than those of gas energy. The carbon emissions of rural energy consumption experienced three stages of development, and the carbon emission intensity showed a downward trend as a whole. The spatial distribution pattern of total carbon emissions present an "adder" distribution, and the spatial agglomeration phenomenon gradually strengthens with the passage of time. (2) The Gini coefficient of China's rural energy consumption carbon emission intensity shows a trend of "Inverted N-shaped." The Gini coefficient of carbon emission intensity in the eastern and northeastern regions shows an increasing trend, while the Gini coefficient of carbon emission intensity in the western and central regions shows a downward trend. The super variable density is the main source of carbon emission intensity difference. The peak value of the main peak of the nuclear density curve of the carbon emission intensity increased significantly, the bimodal form evolved into a single peak form, and the density center moved to the left. (3) The carbon emission intensity of rural energy consumption in the whole, central, and western regions of China has the characteristic of σ convergence, while the carbon emission intensity in the eastern and northeastern regions does not have the characteristic of σ convergence. There is a significant spatial positive correlation in the carbon emission intensity, there is also a significant β convergence characteristic, the speed of conditional β convergence is significantly higher than that of absolute β convergence, and the spatial interaction will further improve the convergence speed. Industrial structure, industrial agglomeration, and energy efficiency will increase the convergence speed. In terms of sub-regions, the conditional convergence rate of carbon emission intensity in the four regions shows a decreasing trend in the northeast, central, eastern, and western regions.

The reasonable spatial layout of industries is crucial to carbon reduction and high-quality economic development. This paper establishes a research paradigm for optimizing the industrial spatial layout and high-quality development in the context of carbon peaking. Based on the perspectives of industrial transfer, the static agglomeration index, dynamic agglomeration index, industrial gradient coefficient, and low-carbon competitiveness index are used to analyze industrial agglomeration, competition status, and low-carbon competitiveness. Taking the Great Bend of the Yellow River (the Bend) as an example, we analyze the current situation in industrial development, guide the orderly transfer of industry, and optimize the spatial layout of industries to achieve high-quality economic development. The results show that resource- and capital-intensive industries have obvious advantages in agglomeration, competitive edge, and low-carbon competitiveness, while labor- and technology-intensive industries have weak advantages. The spatial layout of agglomerated industries was analyzed across four types of factor-intensive industries; these industries are the focus of industrial layout in the Bend. Promising industries were observed in all types of factor-intensive industries except capital-intensive industries, and these industries should be cultivated carefully in all provinces. Scale industries were mainly observed in resource- and capital-intensive industries; these industries should be transformed and upgraded to control the total amount and intensity of carbon emissions. The study’s findings provide a basis for optimizing the spatial layout of industries and reducing carbon emissions through industrial transfer in the context of carbon peaking. The relevant industries should be transformed and upgraded to control the total amount and intensity of carbon emissions.

To accurately measure the carbon emission intensity of tourism, a comprehensive measurement method is proposed in this study. This method combines statistical data and standard deviation ellipse analysis, which can reflect the actual situation of carbon emission in tourism more comprehensively. The spatial autocorrelation of regional tourism is obtained by global Moran's I index and local Moran's I index, and the spatial and temporal evolution characteristics of tourism carbon emission intensity are extracted by standard deviation ellipse analysis. By calculating the consumption stripping coefficient, carbon emission intensity and total carbon emission of tourism, the carbon emission intensity of tourism is calculated. China is divided into eastern, central and western regions, and the carbon emission level and intensity in the region are calculated. The results show that: (1) from 2012 to 2021, the carbon emissions of tourism in various regions generally showed an increasing trend, but the carbon emissions in the eastern region were the highest. (2) From 2018 to 2021, the carbon emission intensity of tourism in different regions is basically the same, and the research period shows a certain downward trend. (3) The accuracy of calculating the carbon emission intensity of tourism in each region obtained by this method can reach 86.5%.

Carbon emission intensity is an important indicator to evaluate the urban development level. Taking Fuzhou, a low-carbon pilot city of China in Jiangxi Province as an example, based on the current levels of energy consumption and carbon emissions, this paper proposes the concept of “the marginal effect of carbon emission intensity” and analyzes the eleven factors influencing the decline of carbon emission intensity in low-carbon cities. Results show that the rise of carbon emissions in industrial sectors are the main reason for the abnormal increase in the carbon emission amount and intensity of Fuzhou, and that the coal consumption level, GDP and electricity sent out to other cities are the key factors influencing the decline of carbon emission intensity. Based on the analysis, countermeasures and suggestions are put forward as a reference for the municipal government in the construction of low-carbon cities.

How do environmental regulation and foreign investment behavior affect green productivity growth in the industrial sector? An empirical test based on Chinese provincial panel data

Carbon emissions and the rising effect of trade openness and foreign direct investment: Evidence from a threshold regression model

The characteristics of common prosperity include harmonious relationships between humans and the environment, as well as sustainable economic and social growth. The process of achieving common prosperity will necessarily have an impact on carbon emissions. In this article, panel statistics collected from 30 Chinese provinces and cities between the years 2006 and 2020 are utilized to assess the level of common prosperity and the intensity of carbon emissions in China. Then the SDM model is applied to explore the effects of the common prosperity level on the intensity of carbon emissions. The findings reveal that: (i) The common prosperity level in China has shown an increasing tendency. Between 2006 and 2020, the mean level of common prosperity increased from 0.254 to 0.486. From the regional perspective, eastern China has seen greater levels of common prosperity than central China, while central China has experienced greater levels of common prosperity than western China; regional disparities in the degree of common prosperity are substantial among Chinese provinces from 2006 to 2020; the common prosperity level is relatively high in economically developed provinces and relatively low in economically backward provinces. (ii) China's carbon emission intensity shows a continuous downward tendency. The annual average intensity of China's carbon emissions decreased from 4.458 in 2006 to 2.234 in 2020. From the regional perspective, the three main regions' carbon emission intensity likewise exhibits a decline in tendency between 2006 and 2020; still, western China continues to have the greatest carbon emission intensity, following central China, while eastern China has the smallest; however, certain provinces, notably Inner Mongolia and Shanxi, continue to have high carbon emission intensity. (iii) China's common prosperity level and carbon emission intensity both exhibit positive spatial autocorrelation at a 1% significant level under the adjacency matrix. The spatial agglomeration effect is significant, and adjacent provinces can affect each other. (iv) The SDM (Spatial Durbin Model) model test with fixed effects finds that the increase in the level of common prosperity suppresses the intensity of carbon emissions in the local area and neighboring regions. (v) The mediating effects model indicates that the process of common prosperity suppresses carbon emission intensity through high-quality economic development, narrowing the income disparity, and the development of a sharing economy.