The evolution characteristics and influence factors of carbon productivity in China's industrial sector: from the perspective of embodied carbon emissions

Measurement and driving factors of carbon productivity in China's provinces: From the perspective of embodied carbon emissions

Carbon productivity incorporates economic development and carbon emissions within a unified framework for measuring the economic value per unit carbon emissions. In the context of climate change, improving carbon productivity is of great value for promoting low-carbon development in a country or region. From the perspective of embodied carbon emissions, this study constructs an embodied carbon productivity (ECP) index and uses the Logarithmic Mean Divisia Index decomposition method to study the evolution trends and driving factors of ECP in China's provinces based on China Interregional Input-Output Tables for 2002, 2007, 2012, and 2017. The following results were obtained: First, China's overall ECP showed a continuously increasing trend during the entire period, with the energy efficiency factor playing the largest role among all driving factors. Second, the ECP in 19 of the 30 Chinese provinces continued to increase and the contributions of energy emission ratio, ECP per capita, and population size factors to the increase in ECP presented evident disparities among different provinces. Third, the ECP in three major regions ranged from high to low in the order of East, Central, and West, with the largest growth in the Central, followed by the West, with the smallest in the East. Based on the analysis of research results, we proposed relevant policy recommendations to further improve China's ECP and achieve low-carbon economy.

A spatiotemporal analysis of the association between carbon productivity, socioeconomics, medical resources and cardiovascular diseases in southeast rural China

Introduction

With China's rapid industrialization and urbanization, China has been increasing its carbon productivity annually. Understanding the association between carbon productivity, socioeconomics, and medical resources with cardiovascular diseases (CVDs) may help reduce CVDs burden. However, relevant studies are limited.

Objectives

The study aimed to describe the temporal and spatial distribution pattern of CVDs hospitalization in southeast rural China and to explore its influencing factors.

Methods

In this study, 1,925,129 hospitalization records of rural residents in southeast China with CVDs were analyzed from the New Rural Cooperative Medical Scheme (NRCMS). The spatial distribution patterns were explored using Global Moran's I and Local Indicators of Spatial Association (LISA). The relationships with influencing factors were detected using both a geographically and temporally weighted regression model (GTWR) and multiscale geographically weighted regression (MGWR).

Results

In southeast China, rural inpatients with CVDs increased by 95.29% from 2010 to 2016. The main groups affected were elderly and women, with essential hypertension (26.06%), cerebral infarction (17.97%), and chronic ischemic heart disease (13.81%) being the leading CVD subtypes. The results of LISA shows that central and midwestern counties, including Meilie, Sanyuan, Mingxi, Jiangle, and Shaxian, showed a high-high cluster pattern of CVDs hospitalization rates. Negative associations were observed between CVDs hospitalization rates and carbon productivity, and positive associations with per capita GDP and hospital beds in most counties (p < 0.05). The association between CVDs hospitalization rates and carbon productivity and per capita GDP was stronger in central and midwestern counties, while the relationship with hospital bed resources was stronger in northern counties.

Conclusion

Rural hospitalizations for CVDs have increased dramatically, with spatial heterogeneity observed in hospitalization rates. Negative associations were found with carbon productivity, and positive associations with socioeconomic status and medical resources. Based on our findings, we recommend low-carbon development, use of carbon productivity as an environmental health metric, and rational allocation of medical resources in rural China.

Do biased technological advances affect carbon productivity of service sector: Evidence from China

Increasing carbon productivity is regarded as one of the significant ways to strike a balance between national economic growth and environmental protection. As the proportion of China's service sector in the national economy rises and the severity of environmental pollution increases, the matter of carbon productivity in service sector required to be explored in depth. This paper focuses on the biased technological advances index of China's service sector and its impact on carbon productivity by constructing a two-layer nested CES production function, proposing policy countermeasures to raise service sector's carbon productivity in China, which is of great practical significance in reducing carbon emissions of China's service sector, improve carbon productivity of China's service sector and promote the green transformation and sustainable development of China's service sector. The results are as follows. (1) The average value of the biased technological advances index of services in China is negative, indicating that technological advances of services in China are biased towards non-energy elements. The biased technological advances indexes of China's service sector in the western, middle and eastern regions are also negative, and the index of the eastern region is the smallest, indicating that the technological advances of the service sector in the eastern region are biased towards non-energy elements to the highest extent. (2) There is a negative correlation between the biased technological advances index and carbon productivity of services in China. In the western, middle and eastern regions of China, the bias of technological advances in eastern China has the greatest effect on China's productivity of carbon in service sector. The policy implication is that in order to increase China's services' productivity of carbon, it is essential to reduce the biased technological advances index of China's service sector.

Empirical research on household consumption carbon emissions and key impact factors in urban and rural China

The analysis of household consumption carbon emissions (HCCEs), a significant source of CO₂ emissions, is essential to achieving China's carbon peak before 2030 and carbon neutrality before 2060. Based on the calculation of urban and rural HCCEs during 2005-2019, the differences between urban and rural areas, spatial-temporal pattern and agglomeration characteristics of HCCEs were analyzed, and the panel quantile STIRPAT model was constructed to empirically test the influence of socioeconomic factors on urban and rural HCCEs at different quantile levels. The results indicate that, first, China's HCCEs are generally growing, indirect HCCEs are more than direct HCCEs, urban HCCEs are far more than rural, and the gap has a growing trend. Second, the urban and rural HCCEs have significant disequilibrium and agglomeration characteristics in space, and high-high and low-low agglomerations dominated the local region. Third, household size and the number of patent application authorizations increase the urban and rural HCCEs, while the consumption capacity and consumption structure inhibit the urban and rural HCCEs. In addition, the level of education also has an inhibitory effect on the rural HCCEs, while the aging degree of the population has a significant positive impact on the rural HCCEs when it is only at the 90th percentile. Finally, it is suggested to formulate differentiated emission reduction policies.

Driving factors of spatial-temporal difference in China's transportation sector carbon productivity: an empirical analysis based on Geodetector method

Carbon productivity is the core index to measure the performance of carbon emission reduction. Exploring the driving factors of the spatial-temporal differences in China's transportation sector, carbon productivity (TSCP) is conducive to the low-carbon sustainable development of the transportation sector. Based on the calculation of TSCP in 30 provinces in China from 2000 to 2019, we use time series, spatial visualization, and Dagum Gini coefficient to reveal the characteristics of spatial-temporal evolution and regional differences of TSCP, and uses Geodetector to identify the driving factors that affecting the spatial-temporal differences of TSCP. The results are as follows: (1) from 2000 to 2019, China's TSCP shows a U-shaped change trend of "decline to rise," and shows a spatial pattern of "high in the eastern and central, low in the western". (2) There are obvious regional differences in China's TSCP. The differences within each region show the trend of "eastern > central > western," while the differences between regions show the trend of "central-western > eastern-western > eastern-central," and the differences between regions are the main reason for the overall differences. (3) The spatial-temporal differences in China's TSCP are affected by many factors, such as social economy and self-endowment. Overall, energy intensity, foreign trade, technological innovation level, energy structure, and industrial structure are the dominant factors. Additionally, the interaction between the driving factors enhances the impact on the spatial-temporal differences of TSCP. Finally, according to the analysis results, some policy suggestions are put forward to improve TSCP.

The evolutionary characteristics and influencing factors of total carbon productivity: evidence from China

In order to systematically understand the evolution of total factor carbon productivity and explore its influence mechanism, based on panel data of 30 Chinese provinces from 2005 to 2019, the slacks-based measure of directional distance functions model and the Luenberger index are used to estimate the evolution of total factor carbon productivity, and the SYS-GMM model is constructed to explore the drivers of total factor carbon productivity and its influence effect. The results show that from 2005 to 2019, the overall level of total factor carbon productivity was low, but its growth index and decomposition term both showed an increasing trend; the development of total factor carbon productivity has regional differences. Only the eastern, northern, and middle Yellow River economic regions experience positive growth in total factor carbon production. The downward trend of total factor carbon productivity is most significant in the northwest and southwest economic regions, with - 2.577% and - 1.463%, respectively; improvements in scale technology are the main reasons for improving total factor carbon productivity across time and regions; economic growth and environmental regulations contribute to total factor carbon productivity at 1% significance level, and industrial structure has a negative impact. Foreign direct investment inhibits total factor carbon productivity, but the effect is not significant. Based on these findings, this paper provides an effective reference for achieving the goal of low-carbon sustainable development and improving total factor carbon productivity.

Influence mechanisms and spatial spillover effects of industrial agglomeration on carbon productivity in China's Yellow River Basin

The ecological protection and high-quality development of the Yellow River Basin have become major national strategies in China. Therefore, reducing carbon emissions in the Yellow River Basin through efficient industrial agglomeration is necessary for achieving the goals of carbon peak by 2030 and carbon neutrality by 2060. The Yellow River Basin is an important base for energy, chemicals, raw materials, and industry in China, making it important to study the effects of different industrial agglomeration types on carbon productivity from the perspective of agglomeration externalities. Therefore, taking 2009-2019 panel data for prefecture-level cities in the Yellow River Basin, this study uses a spatial Durbin model to investigate the spatial spillover effects of industrial agglomeration (i.e., specialized, diversified, and competitive agglomeration) on carbon productivity. Furthermore, the moderating effects of urbanization level and environmental regulation are analyzed. The results reveal, first, the existence of spatial correlation in carbon productivity across different cities in the Yellow River Basin. Second, diversified and competitive agglomeration significantly increase carbon productivity, although competitive agglomeration has beggar-thy-neighbor spillover effects. Meanwhile, the effect of specialized agglomeration is not significant. Third, the effects of different types of industrial agglomeration differ significantly between cities in different locations and with different resource endowments. Fourth, urbanization level and environmental regulation have different moderating effects in the relationship between different types of industrial agglomeration and carbon productivity. These findings provide evidence for further developing rational industrial agglomeration patterns to enhance carbon productivity in the Yellow River Basin.

Identification of Key Carbon Emission Industries and Emission Reduction Control Based on Complex Network of Embodied Carbon Emission Transfers: The Case of Hei-Ji-Liao, China

Similar to the problems surrounding carbon transfers that exist in international trade, there are severe carbon emission headaches in regional industrial systems within countries. It is essential for emission reduction control and regional industrial restructuring to clarify the relationship of carbon emissions flows between industrial sectors and identify key carbon-emitting industrial sectors. Supported by the input-output model (I-O model) and social network analysis (SNA), this research adopts input-output tables (2017), energy balance sheets (2021) and the energy statistics yearbooks (2021) of the three Chinese provinces of Hei-Ji-Liao to construct an Embodied carbon emission transfer network (ECETN) and determine key carbon-emitting industrial sectors with a series of complex network measurement indicators and analysis methods. The key abatement control pathways are obtained based on the flow relationships between the chains in the industrial system. The results demonstrate that the ECETNs in all three provinces of Hei-Ji-Liao are small-world in nature with scale-free characteristics (varying according to the power function). The key carbon emission industry sectors in the three provinces are identified through centrality, influence, aggregation and diffusion, comprising coal mining, the chemical industry, metal products industry, machinery manufacturing and transportation in Liaoning Province; coal mining, non-metal mining, non-metal products, metal processing and the electricity industry in Jilin Province; and agriculture, metal processing and machinery manufacturing in Heilongjiang. Additionally, key emission reduction control pathways in the three provinces are also identified based on embodied carbon emission flow relationships between industry sectors. Following the above findings, corresponding policy recommendations are proposed to tackle the responsibility of carbon reduction among industrial sectors in the province. Moreover, these findings provide some theoretical support and policy considerations for policymakers.

Research on carbon emission measurement and low-carbon path of regional industry

As industry is the world's leading carbon emitter, promoting industrial carbon reduction is of key significance to carbon peak and carbon neutrality. Using a data-driven method, based on the collection and processing of relevant data from statistical yearbooks and others, we analyze the efficiency and amount of carbon emission of each industrial sector after processing multi-dimensional data by the improved IPCC EF method of calculating carbon emissions. In addition, we adopt the LMDI decomposition method for data modeling to measure the contribution of energy efficiency, industrial structure, GDP per capita, and population size to carbon emission changes, to identify targets for industrial carbon reduction, and to propose a targeted optimization path for carbon emission. We show how the method is implemented by taking the statistics of Anhui Province from 2010 to 2019 as an example and advises on an optimization path for carbon emission in Anhui Province. This study is of both theoretical and practical significance as it provides theoretical and methodological support for the low-carbon development of the regional industry, and provides a reference for other countries and regions to explore the path of low-carbon and environment-friendly green transformation and upgrading.

Research on the interactive response relationship between thermal power carbon emission and industrial structure in Western China

The thermal power industry takes the important social responsibility of energy conservation and environmental protection. The carbon emissions made by the thermal power industry are affected by the industrial structure. In this paper, the carbon emission of thermal power generation is divided into three links: energy combustion, desulfurization process, and power transportation. The total carbon emission of thermal power in 11 provinces in western China from 2000 to 2017 is calculated. Combined with industrial reform, this paper constructs a panel data fixed effect model to systematically analyze the interactive response relationship between thermal power carbon emission and industrial structure in the western region. The research shows that due to the continuous expansion of hydropower, wind power, and other new energy power generation scale and the improvement of energy efficiency in the western region, the growth trend of thermal power carbon emission has slowed down since 2010. The industrial development pattern is the main driving force of regional economic development, and the secondary industry in the western region is the main driving factor of thermal power carbon emission. High quality economic development in the western region can be promoted through technological upgrading, new energy development, and industrial multi-mode operation.

How does power technology innovation affect carbon productivity? A spatial perspective in China

Power technology innovation has been positioned as an effective way to contribute to China's carbon productivity. However, limited empirical evidence exists on the impact of power technology innovation on carbon productivity. Thus, based on the annual panel dataset of 30 China's provinces from 2001 to 2019, this study explored whether and how power technology innovation promotes or impedes the improvement of carbon productivity. First, carbon productivity in the framework of total factor was calculated based on the metafrontier Malmquist-Luenberger productivity index. Second, the effect of power technology innovation on carbon productivity was investigated using the spatial Durbin model. And we also examined whether heterogeneous power technology innovations have a synergistic effect on carbon productivity. Third, influence mechanism of power technology innovation affecting carbon productivity was identified. Results show that (1) there are notable differences in China's provincial carbon productivity, which is characterized by the spatial correlation. (2) Local power technology innovation has a promotion effect on carbon productivity in both local and neighboring provinces. Moreover, the promotion effect of breakthrough power technology innovation is stronger than that of incremental power technology innovation. (3) Catching-up Effect and Innovation Effect are important transmission channels through which power technology innovation improves carbon productivity. Finally, policy recommendations are provided.

Transportation moderation in agricultural sector sustainability - a robust global perspective

The agriculture sector creates nearly a quarter of the total GHG emissions globally as production and transportation activities in the agriculture sector mostly use fossil fuels, creating carbon emissions. In this regard, it is highly important to study the environmental sustainability of agriculture sector growth by using the theory of environmental Kuznets curve (EKC). Furthermore, this research study is aimed to assess the moderation role of transportation competitiveness in determining the carbon emissions of transportation sector by using agriculture sector value addition. The study uses panel quantile regression technique for data analysis of 121 countries by covering time period from 2008 to 2018. The study results validated the agricultural EKC across four different quantile groups based on carbon emissions of transport sector. The moderation of transportation competitiveness is observed in changing the turning point and flattening of agricultural EKC indicating the early achievement of maturity. The quality of institutions and planned increase of population can help reduce carbon emissions of transportation sector. The moderation of transportation competitiveness implicates the importance of planning and efficiently operating the transportation sector to mitigate carbon emissions.

Coupling Coordination and Spatiotemporal Evolution between Carbon Emissions, Industrial Structure, and Regional Innovation of Counties in Shandong Province

Industrial structure and regional innovation have a significant impact on emissions. This study explores, from the multivariate coupling and spatial perspectives, the degree of coupling coordination between three factors: industrial structure, carbon emissions, and regional innovation of 97 counties in Shandong Province, China from 2000 to 2017. On the basis of global spatial autocorrelation and cold and hot spots, this article analyzes the spatial characteristics and aggregation effects of coupled and coordinated development within each region. The results are as follows. (1) The coupling degree between carbon emissions, industrial structure, and regional innovation in these counties fluctuated upward from 2000 to 2017. Coupling coordination progressed from low coordination to basic coordination. Regional differences in coupling coordination degree are evident, showing a stepped spatial distribution pattern with high levels in the east and low levels in the west. (2) During the study period, the coupling coordination showed a positive correlation in spatial distribution. Moran’s I varies from 0.057 to 0.305 on a global basis. Spatial clustering is characterized by agglomeration of cold spots and hot spots. (3) The coupling coordination exhibited significant spatial differentiation. The hot spots were distributed in the eastern part, while the cold spots were located in the western part. The results of this study suggest that the counties in Shandong Province should promote industrial structure upgrades and enhance regional innovation to reduce carbon emissions.

Spatio-Temporal Patterns of CO2 Emissions and Influencing Factors in China Using ESDA and PLS-SEM

Controlling carbon dioxide (CO2) emissions is the foundation of China’s goals to reach its carbon peak by 2030 and carbon neutrality by 2060. This study aimed to explore the spatial and temporal patterns and driving factors of CO2 emissions in China. First, we constructed a conceptual model of the factors influencing CO2 emissions, including economic growth, industrial structure, energy consumption, urban development, foreign trade, and government management. Second, we selected 30 provinces in China from 2006 to 2019 as research objects and adopted exploratory spatial data analysis (ESDA) methods to analyse the spatio-temporal patterns and agglomeration characteristics of CO2 emissions. Third, on the basis of 420 data samples from China, we used partial least squares structural equation modelling (PLS-SEM) to verify the validity of the conceptual model, analyse the reliability and validity of the measurement model, calculate the path coefficient, test the hypothesis, and estimate the predictive power of the structural model. Fourth, multigroup analysis (MGA) was used to compare differences in the influencing factors for CO2 emissions during different periods and in various regions of China. The results and conclusions are as follows: (1) CO2 emissions in China increased year by year from 2006 to 2019 but gradually decreased in the eastern, central, and western regions. The eastern coastal provinces show spatial agglomeration and CO2 emission hotspots. (2) Confirmatory analysis showed that the measurement model had high reliability and validity; four latent variables (industrial structure, energy consumption, economic growth, and government management) passed the hypothesis test in the structural model and are the determinants of CO2 emissions in China. Meanwhile, economic growth is a mediating variable of industrial structure, energy consumption, foreign trade, and government administration on CO2 emissions. (3) The calculated results of the R2 and Q2 values were 76.3% and 75.4%, respectively, indicating that the structural equation model had substantial explanatory and high predictive power. (4) Taking two development stages and three main regions as control groups, we found significant differences between the paths affecting CO2 emissions, which is consistent with China’s actual development and regional economic pattern. This study provides policy suggestions for CO2 emission reduction and sustainable development in China.