Spatial Correlation Network and Driving Effect of Carbon Emission Intensity in China’s Construction Industry

To explore the spatial network structure characteristics and driving effects of carbon emission intensity in China's construction industry, the investigation combined the modified gravity model and social network analysis method to deeply analyze the spatially associated network structure characteristics and driving effects of carbon emission intensity in China's construction industry, based on the measurement of carbon emission data of China's construction industry from 2006 to 2017. The results show that the regional differences of carbon emission of construction industry are significant, and the carbon emission intensity of construction industry show a fluctuation trend. The overall network of carbon emission intensity shows an obvious “core-edge” state, the hierarchical network structure is gradually broken. Economically developed provinces generally play a leading role in the network, and play an intermediary role to guide other provinces to develop together with them. Among the network blocks, most of the blocks play the role of “brokers”. The block with the leading economic development has a strong influence on the other blocks. The increase of network density, the decrease of network hierarchy and network efficiency will reduce the construction carbon emission intensity.

Can the Financial Industry ‘Anchor’ Carbon Emission Reductions?

Existing research is ambiguous about the relationship between the financial industry development scale and carbon emission reduction targets. Therefore, using data from 30 provinces and municipalities directly under the central government (excluding Tibet, Hong Kong, Macao, and Taiwan) from 2009–2018, this study divides the reduction targets into emission quantity and intensity to investigate this relationship. Using the improved STIRPAT equation, the pooled OLS and other estimation technique in robustness test, we found that the financial industry development scale is positively related to emission quantity and negatively related to emission intensity. The financial industry development scale inhibits carbon emission intensity through the mediating role of the technology market development degree, which also has a moderating effect on the scale. The study also discusses the regional differences in the scale's impact on carbon emission intensity, its compensation effect on the economic loss caused by carbon emissions, and the positive influence of policy implementation on carbon emission intensity. We provide suggestions to reduce carbon emissions and achieve carbon neutrality.

Construction and Application Analysis of Carbon Emission Influence Factor Model of Energy Consumption in Mining Industry

With the proposal of China’s “double carbon goal,” as a high energy-consuming industry, it is urgent for the mining industry to adopt a low-carbon development strategy. Therefore, in order to better provide reasonable suggestions and references for the low-carbon development of mining industry, referring to the methods and parameters of the 2006 IPCC National Greenhouse Gas Inventory Guidelines and China’s Provincial Greenhouse Gas Inventory Preparation Guidelines (Trial), a carbon emission estimation model is established to estimate the carbon emission of energy consumption of China's mining industry from 2000 to 2020. Then, using the extended Kaya identity, the influencing factors of carbon emission in mining industry are decomposed into energy carbon emission intensity, energy structure, energy intensity, industrial structure, and output value. On this basis, an LMDI model is constructed to analyze the impact of five factors on carbon emission from mining industry. The research shows that the carbon emission and carbon emission intensity of energy consumption in China’s mining industry first rise and then fall and then rise slightly. The carbon emission intensity in recent three years is about 2 tons/10000 yuan. The increase in output value is the main factor to increase carbon emission. The reduction in energy intensity is the initiative of carbon emission reduction. The current energy structure of mining industry is not conducive to carbon emission reduction.

Decoupling and Decomposition Analysis of Agricultural Carbon Emissions: Evidence from Heilongjiang Province, China

Ensuring food security and curbing agricultural carbon emissions are both global policy goals. The evaluation of the relationship between grain production and agricultural carbon emissions is important for carbon emission reduction policymaking. This paper took Heilongjiang province, the largest grain-producing province in China, as a case study, estimated its grain production-induced carbon emissions, and examined the nexus between grain production and agricultural carbon emissions from 2000 to 2018, using decoupling and decomposition analyses. The results of decoupling analysis showed that weak decoupling occurred for half of the study period; however, the decoupling state and coupling state occurred alternately, and there was no definite evolving path from coupling to decoupling. Using the log mean Divisia index (LMDI) method, we decomposed the changes in agricultural carbon emissions into four factors: agricultural economy, agricultural carbon emission intensity, agricultural structure, and agricultural labor force effects. The results showed that the agricultural economic effect was the most significant driving factor for increasing agricultural carbon emissions, while the agricultural carbon emission intensity effect played a key inhibiting role. Further integrating decoupling analysis with decomposition analysis, we found that a low-carbon grain production mode began to take shape in Heilongjiang province after 2008, and the existing environmental policies had strong timeliness and weak persistence, probably due to the lack of long-term incentives for farmers. Finally, we suggested that formulating environmental policy should encourage farmers to adopt environmentally friendly production modes and technologies through taxation, subsidies, and other economic means to achieve low-carbon agricultural goals in China.

Impact of Green Innovation Efficiency on Carbon Emission Reduction in the Guangdong-Hong Kong-Macao GBA

Climate change has become a global issue of general concern to human society. It is not only an environmental issue, but also a development issue. As the second largest economy in the world, China has adhered to its commitments in the Paris Agreement and formulated a series of autonomous action targets. In this context, scholars have done a lot of research focusing on carbon emission reduction, but have neglected the spatial correlation of carbon emission, and lack of research on carbon emission reduction in urban agglomerations. The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) has been at the forefront of China in terms of economy, politics, ecology, and civilization by taking advantage of the “one country, two systems” policy. This article innovatively proposes that there is a non-linear relationship between the efficiency of green innovation and the carbon emission intensity of the Guangdong-Hong Kong-Macao GBA, and has passed quantitative verification. Based on the panel data of the Guangdong-Hong Kong-Macao GBA from 2009 to 2019, we used the super-efficiency slacks-based measure (SBM) model to measure the efficiency of green innovation. We used the global Moran index and Theil index to discuss the spatial correlation of carbon emissions and regional differences in carbon emission intensity in the Guangdong-Hong Kong-Macao GBA, respectively. Then, we used the threshold model to verify the nonlinear relationship between the efficiency of green innovation and the intensity of carbon emissions in the Guangdong-Hong Kong-Macao GBA. The results of the study found that the green innovation efficiency of the Guangdong-Hong Kong-Macao GBA is increasing overall, carbon emissions have a certain spatial correlation, and the correlation is low overall. The impact of green innovation efficiency on carbon emission intensity has a non-linear relationship and there is an “inverted U” pattern between the two, and there is an inflection point in green innovation efficiency. Based on this, this article proposes carbon emission reduction measures within a reasonable range, and looks forward to future research directions and complement the research deficiencies.

Factors Affecting Wind Power Efficiency: Evidence from Provincial-Level Data in China

As a significant energy consumer, China is under tremendous pressure from the international community to address climate change issues by reducing carbon emissions; thus, the use of clean energy is imperative. Wind power is an essential source of renewable energy, and improving the efficiency of wind power generation will contribute substantially to China’s ability to achieve its energy-saving and emission reduction goals. This paper measured the wind power efficiency of 30 provinces in China from 2012 to 2017 using the data envelopment analysis (DEA) method. Moran’s I index and the spatial Durbin model were applied to analyse the spatial distribution of the wind power efficiency and the spatial effects of influencing factors. The results show obvious differences in the spatial distribution of wind power efficiency in China; specifically, the wind power efficiency in the eastern and western regions is higher than that in the central areas. Moreover, wind power efficiency has a significant positive spatial correlation between regions: the eastern and western regions show certain high-high clustering characteristics, and the central area shows certain low-low clustering characteristics. Among the influencing factors, the fixed asset investment and carbon emission intensity of the wind power property have a negative impact on the efficiency of regional wind power production, while the urbanization process and carbon emission intensity have significant spatial spillover effects. The optimization of the economic structure, technological innovation and the construction of energy infrastructure are expected to improve the regional wind power efficiency. The results present a new approach for accurately identifying the spatial characteristics of wind power efficiency and the spatial effects of the influencing factors, thus providing a reference for policymakers.