Energy investment, economic growth and carbon emissions in China—Empirical analysis based on spatial Durbin model

The massive use of energy has caused a rapid increase in global carbon dioxide emissions, resulting in a series of environmental problems such as climate warming. Investment in the energy industry can guide funds into green and clean production, reduce carbon emissions in the energy industry, and promote the green development of the energy industry. This paper considers the energy, the environment, the economy, and other factors and focuses on energy consumption and investment structure. Taking 30 provinces in China as research samples, a dynamic spatial Durbin model is established. The results show that the first-order term of carbon emissions has a driving force of 0.5068% for current carbon emissions at a significance level of 1% and that the increase in current carbon emissions will lead to a continued increase in carbon emissions in the next period. The increase in the carbon emissions of neighbouring provinces will increase their carbon emissions through the spatial spillover effect. Whether in the short term or long term, the increase in energy investment and the optimization of the energy investment structure can reduce carbon emissions. The above conclusions can provide a reference for the formulation of government environmental policies.

Global warming continues to be an intimidating factor for environmental protection, and reducing carbon emissions is an effective way to deal with the phenomenon. However, the energy sector is a significant contributor to greenhouse gas emissions. Therefore, investment in environmental regulations and research and development (R&D), is critical for fostering a low-carbon growth model. This study focuses on 30 provinces in China from 2004 to 2019. We used the spatial Durbin model to investigate how the spatial spillover effect of R&D and environmental regulation impacts carbon emissions. In addition, we applied the dynamic threshold panel model to mitigate potential problems of endogeneity. The results reveal that carbon emissions have a considerable spatial correlation in both temporal and spatial dimensions, exhibiting high and low-value accumulation characteristics. Furthermore, the combined effect of R&D intensity, environmental regulation, and energy consumption were found to contribute to the increase in carbon emissions across China's provinces, and they also suggest different influencing mechanisms. The spillover effects of increased carbon emissions in neighboring regions also contribute to the increase in local carbon emissions. The study also found that R&D and stringent environmental regulations measures strongly moderate the link between energy consumption and carbon emissions. In promoting carbon reduction, by breaking the dynamic equilibrium in China, the provincial investment outflow on R&D intensity could be optimized, and the regional levels should focus more on tightening environmental regulatory measures and promoting the development of energy-conserving technologies.

With the accelerated development of the aging trend in Chinese society, the aging problem has become one of the key factors affecting sustainable economic and social development. Given the importance of controlling carbon emissions for achieving global climate goals and China's economic transformation, studying the spatial and temporal effects of population aging on carbon emissions and their pathways of action is of great significance for formulating low-carbon development strategies adapted to an aging society. This paper aims to explore the spatial-temporal effects of population aging on carbon emissions, identify the key pathways through which aging affects carbon emissions, and further explore the variability of these effects across different regions. The findings will provide theoretical support and empirical evidence for government departments to formulate policies to promote the coordinated development of a low-carbon society and an aging society. Based on the panel data of 30 provinces in China from 2004 to 2022, this paper systematically investigates the impact of population aging on carbon emission intensity from both spatial and temporal dimensions by using the spatial Durbin model and the mediating effect model. The direct effect of aging on carbon emission intensity, the spatial spillover effect, and the indirect effect through mediating variables such as residents' consumption, environmental regulation, and new urbanization are analyzed in depth. The study found that population aging in China has significant spatial and temporal effects on carbon emissions. From the spatial dimension, there is a significant spatial spillover effect of the effect of aging on carbon emissions, and aging reduces local carbon emissions but increases carbon emissions in adjacent regions. From the time dimension, the effect of aging on carbon emissions shows a stage characteristic, initially it will reduce carbon emissions, but with the deepening of aging, its effect may tend to weaken. In addition, this study identifies a number of key pathways through which aging affects carbon emissions, including reducing residential consumption, promoting new urbanization, and increasing the intensity of environmental regulations. Finally, this study explores the regional heterogeneity of the impact of aging on carbon emissions and its mechanism of action. This study is instructive: first, the complex impact of population aging on carbon emissions should be fully recognized to formulate a comprehensive low-carbon development strategy; second, attention should be paid to the spatial spillover effect of aging on carbon emissions to strengthen inter-regional cooperation and coordination; and lastly, differentiated low-carbon policies should be formulated to address the characteristics of aging in different regions and stages in order to promote the synergistic development of a low-carbon society and an aging society.

The impact of private sector energy investment, innovation and energy consumption on China's carbon emissions

In the context of the "dual carbon" strategy, the transportation industry actively seeks a new development path of low-carbon transformation, which is one of the hot spots in China's carbon emission reduction. Based on the Tapio decoupling and logarithmic mean Divisia index （LMDI） models, this study analyzed the carbon emission characteristics, decoupling status, and driving factors of China's provincial transportation industry from multiple perspectives, such as overall, time period, and regional decomposition from 2012 to 2021. The results showed that China's total carbon emissions from transport sector exhibited an increasing trend with passing years overall, but growth rate showed decreasing trend. The spatial distribution pattern of carbon emissions from transportation industry was higher in the southeast and lower in northwest. During the past decade, 40.0% of the provinces achieved absolute decoupling between carbon emissions from transportation industry and economic development, and 53.3% of the provinces achieved relative decoupling. From the perspective of the transportation industry, economic growth, population size, and carbon emission coefficient promoted an increase in carbon emissions, while transportation energy intensity and industry scale inhibited the increase in carbon emissions. Therefore, during the process of realizing the "dual carbon" goal in China, each province should formulate differentiated reduction policies in regional carbon emissions according to local conditions, actively assume emission reduction responsibilities, increase efforts to promote the decoupling process, and promote the green and low-carbon transformation of the transportation industry.

Since the reform and opening-up, China has made remarkable achievements in economic growth, but also led to a substantial increase in carbon emissions. The Chinese government has actively formulated energy intensity reduction targets and taken carbon emission reduction measures. The paper investigates the impact of energy intensity reduction targets on carbon emissions using a dynamic spatial Durbin model based on panel data from 30 provinces in China from 2006 to 2019. The results show that energy intensity reduction targets promote the reduction of local carbon emissions, but have a positive spillover effect on carbon emissions in adjacent regions. Meanwhile, green technology innovation has a non-linear moderating effect between energy intensity reduction targets and carbon emissions. Energy intensity reduction targets promote carbon emission reduction when green technology innovation is less than a threshold, while the promotion effect disappears when green technology innovation exceeds a threshold. The mechanism analysis shows that energy consumption structure is a channel through which energy intensity reduction targets affect carbon emissions in both local and adjacent regions. Further research found that peer competitive pressure promotes carbon emission reduction and alleviates pollution spillover, while central assessment pressure increases carbon emissions and aggravates pollution spillover. Based on the above findings, this study provides suggestions for policymakers aiming at carbon emission reduction by implementing target management policies and optimizing target management systems.

Impact of renewable energy investment on carbon emissions in China - An empirical study using a nonparametric additive regression model

Renewable energy consumption is considered as the main form of energy consumption in the future. The carbon emissions produced by renewable energy can be approximately ignored, and renewable energy is essential for regional sustainable development. In this study, we used the Durbin model with panel data to explore the spatial dependence between renewable energy consumption the and carbon emissions of China’s 30 provinces from 1997 to 2017. The results show that: (1) there is a negative spatial correlation between renewable energy consumption and carbon emissions, and “High-Low” areas are mainly concentrated in southern provinces in 1997–2011; (2) the center of gravity of renewable energy consumption moves southwest, which is consistent with the center of gravity of carbon emissions; (3) renewable energy consumption has a significant inhibitory effect on carbon emissions of a local region, but the spatial spillover effect is not significant. Specifically, a 1% increase in renewable energy consumption in a region will reduce carbon emissions by 0.05%. Finally, on the basis of this study, it was proposed to give full play to the advantages of renewable energy in the western region, and further accelerate the development of the renewable energy industry.

This paper expanded the Logarithmic Mean Divisia Index (LMDI) model through the introduction of urbanization, residents’ consumption, and other factors, and decomposed carbon emission changes in China into carbon emission factor effect, energy intensity effect, consumption inhibitory factor effect, urbanization effect, residents’ consumption effect, and population scale effect, and then explored contribution rates and action mechanisms of the above six factors on change in carbon emissions in China. Then, the effect of population structure change on carbon emission was analyzed by taking 2003–2012 as a sample period, and combining this with the panel data of 30 provinces in China. Results showed that in 2003–2012, total carbon emission increased by 4.2117 billion tons in China. The consumption inhibitory factor effect, urbanization effect, residents’ consumption effect, and population scale effect promoted the increase in carbon emissions, and their contribution ratios were 27.44%, 12.700%, 74.96%, and 5.90%, respectively. However, the influence of carbon emission factor effect (−2.54%) and energy intensity effect (−18.46%) on carbon emissions were negative. Population urbanization has become the main population factor which affects carbon emission in China. The “Eastern aggregation” phenomenon caused the population scale effect in the eastern area to be significantly higher than in the central and western regions, but the contribution rate of its energy intensity effect (−11.10 million tons) was significantly smaller than in the central (−21.61 million tons) and western regions (−13.29 million tons), and the carbon emission factor effect in the central area (−3.33 million tons) was significantly higher than that in the eastern (−2.00 million tons) and western regions (−1.08 million tons). During the sample period, the change in population age structure, population education structure, and population occupation structure relieved growth of carbon emissions in China, but the effects of change of population, urban and rural structure, regional economic level, and population size generated increases in carbon emissions. Finally, the change of population sex structure had no significant influence on changes in carbon emissions.

Economic policy and energy policy are two major factors of energy consumption and carbon emissions. The economic factor is external and energy supply structure and efficiency are intrinsic factors. Based on a carbon emissions completely decomposed analysis model, the logarithmic mean Divisia Index (LMDI) system analyzes the impact of carbon emission changes and the contribution rate in China from 1995 to 2010. The decomposition factors include four parts: economies of scale, structure effect, energy intensity effect and carbon intensity effects. Model results show that the contribution rate of the four effects is different and from 1995 to 2010 the greatest factors impacting increases in carbon emissions were economic development (contribution rate of 155%) and industrial structure change (contribution rate of 10.6%). The reduction in carbon emissions was mainly the result of a decline in energy intensity (contribution rate of -63.7%). The increase in carbon emissions in recent years is the result of changes in major economies of scale with 168.2% contribution rate, changes in carbon intensity (contribution rate of 4%) and industrial restructuring (contribution rate of 1.3%) have also contributed to increasing carbon emissions. Energy intensity declined only played a role in reducing carbon emissions (contribution rate -73.5%). These results suggest that China needs to rethink industrial policy and energy development measures, strengthen future energy saving and emission mitigation policies and strengthen investment in low—carbon energy technologies and policy support.

Transportation is an important source of carbon emissions in China. Reduction in carbon emissions in the transportation sector plays a key role in the success of China’s energy conservation and emissions reduction. This paper, for the first time, analyzes the drivers of carbon emissions in China’s transportation sector from 2000 to 2015 using the Generalized Divisia Index Method (GDIM). Based on this analysis, we use the improved Tapio model to estimate the decoupling elasticity between the development of China’s transportation industry and carbon emissions. The results show that: (1) the added value of transportation, energy consumption and per capita carbon emissions in transportation have always been major contributors to China’s carbon emissions from transportation. Energy carbon emission intensity is a key factor in reducing carbon emissions in transportation. The carbon intensity of the added value and the energy intensity have a continuous effect on carbon emissions in transportation; (2) compared with the increasing factors, the decreasing factors have a limited effect on inhibiting the increase in carbon emissions in China’s transportation industry; (3) compared with the total carbon emissions decoupling state, the per capita decoupling state can more accurately reflect the relationship between transportation and carbon emissions in China. The state of decoupling between the development of the transportation industry and carbon emissions in China is relatively poor, with a worsening trend after a short period of improvement; (4) the decoupling of transportation and carbon emissions has made energy-saving elasticity more important than the per capita emissions reduction elasticity effect. Based on the conclusions of this study, this paper puts forward some policy suggestions for reducing carbon emissions in the transportation industry.

Digital economy provides a new opportunity for the development of green logistics, and digital transformation has become an important way for the logistics industry to save energy and reduce emissions. Based on the panel data of 30 provinces in China from 2005 to 2022, the spatial Durbin model, nonlinear intermediary effect model, and nonlinear regulatory effect model were constructed to investigate the carbon emission reduction effect of logistics industry digitization and its internal mechanism and enhancement path. The results showed that： ① The digitalization of the logistics industry had an inverted "U"-shaped effect on carbon emissions. This nonlinear effect had a spatial spillover effect that was stronger than the direct effect. Currently, 17 of China's 30 provinces, including Beijing, Shanghai, Guangdong, Jiangsu, Shandong, Hubei, Anhui, Fujian, Shanxi, Henan, Guangxi, Sichuan, Liaoning, Tianjin, Hunan, Xinjiang, and Inner Mongolia, have already crossed the inflection point, and the rest of the provinces remain on the left side of the inflection point. The conclusion was still valid after a series of robustness tests. Further heterogeneity analysis showed that, from a regional perspective, the local carbon emission reduction effect of logistics digitalization in the central region was the most prominent, and the spatial spillover effect of carbon emission reduction of logistics digitalization in the eastern region was the most prominent. From the perspective of digital input sources, the improvement of digital hardware level could reduce the carbon emissions of the logistics industry, while the impact of digital software on the carbon emissions of the logistics industry was inverted "U"-shaped. Compared with that of digital hardware, digital software had a more prominent carbon reduction effect. ② The results of mechanism analysis showed that green technology innovation and energy use efficiency played a nonlinear mediating role in the impact of logistics digitization on carbon emissions. Specifically, the digitalization of the logistics industry had a "U"-shaped nonlinear impact on green technology innovation and energy utilization efficiency, while the improvement of green technology innovation and energy utilization efficiency was conducive to reducing carbon emissions in the logistics industry. ③ The agglomeration and energy structure optimization of the logistics industry had a significant moderating effect on the carbon emission reduction effect of digitalization, in which the industrial agglomeration had an "amplifier" effect on the carbon emission reduction effect of logistics digitalization, that is, the improvement of the agglomeration level could enhance the inverted "U"-shaped impact of logistics digitalization on carbon emissions. Additionally, the energy structure optimization could slow down the carbon emission increase effect of logistics digitization in the early stage of transformation but could not enhance the carbon emission reduction effect of logistics digitization in the mature stage of transformation. Based on the conclusions of the study, countermeasures were proposed for the green and low-carbon development of the logistics industry in terms of creating a digital logistics ecosystem integrating software and hardware, promoting the utilization of clean energy in the logistics industry and the transformation and upgrading of industrial clusters, and promoting the synergistic low-carbon development of regional logistics. This study deepens the theoretical exploration of the carbon emission reduction mechanism of the logistics digitalization and provides a path choice for China's logistics industry to achieve green and low-carbon transformation under the "dual carbon" vision.

Economic development has largely contributed to the increment of CO2 emission. This study uses spatial econometric models to investigate the relationship between economic growth and carbon emission in China with data of 30 provinces of China during the period of 2000 to 2012. Results show that the relationship between carbon emission and economic growth in China during the recent decade has the development tendency toward an inverse U-shaped curve, approximately confirming the carbon emission’s Kuznets curve hypothesis in China. There exists a significant spatial correlation between carbon emission and economic growth, implying that carbon emission in a province may be influenced by economic growth in adjacent provinces. When economic growth reaches 279.91 million Yuan/km2 GDP (at a comparable price in 2000), the contradiction between economic growth and carbon emission begins to be gradually alleviated. These findings provide new insights and valuable information for reducing carbon emissions in China.

The existing literature tends to overlook the trade‐off between the local government's dual goals of achieving economic growth targets and protecting the environment. This study reveals the adverse effects that local governments may have on the environment under the pressure of economic growth, indicating that an increase in regional economic growth targets is associated with an increase in local carbon emissions. Using data from Chinese prefecture‐level cities from 2000 to 2021, we find a 1% increase in the GDP growth target is associated with an increase in carbon emissions per unit of GDP of 1.6%. The mechanism analysis indicates that a higher regional growth target results in the entry of highly polluting new enterprises and a reduction in environmental protection‐related inputs, which consequently leads to an increase in carbon emissions. The implementation of administrative and market‐based policies, including government policy planning and piloting of emission rights, can mitigate the driving effect of growth targets on carbon emissions. This study provides new evidence on the impact of regional economic growth pressures on carbon emissions and serves as an important reference for governments seeking to formulate effective carbon emission reduction policies.

Spatial and temporal evolution characteristics and spillover effects of China's regional carbon emissions