Can Industrial Co-Agglomeration between Producer Services and Manufacturing Reduce Carbon Intensity in China?

Abstract

Climate change poses unprecedented challenges for humanity. Reducing carbon intensity is an inevitable choice for tackling climate change and promoting sustainable development. China has made some emission reduction commitments in the international community to promote the decoupling of China’s economic development from carbon emissions. The realization of the industrial structure from the “single-wheel drive” of the manufacturing to the “two-wheel drive” economic development model of the service industry and the manufacturing has become a key measure to achieve China’s economic intensive development. According to resource misallocation situation in different regions, this paper explored the impact of the collaborative agglomeration between producer services and manufacturing (hereinafter referred to as industrial co-agglomeration) on carbon intensity. The research results show that the carbon intensity is decreasing year by year, and the degree of intensification of China’s economic growth continues to increase. Moreover, the effect of industrial co-agglomeration to promote carbon emission reduction is significantly limited by the degree of misallocated resources, and there is a double threshold effect. Specifically, in areas where resource allocation is reasonable, industrial co-agglomeration can produce significant agglomeration effects and promote carbon intensity reduction. Once the degree of misallocated resources exceeds a threshold level, the agglomeration effect will turn into a crowding effect, resulting in an inability to reduce carbon intensity. We comprehensively analyzed the driving factors for reducing carbon intensity and proposed policy pathways for achieving China’s carbon intensity target.

Is Technological Innovation Effective for Energy Saving and Carbon Emissions Reduction? Evidence From China

“…There have been many literatures exploring the driving force of carbon emissions at the industry and regions level through quantitative methods, such as threshold regression model [2], [12], autoregressive distributed lag modeling [13], quantile estimates [14], [15], and generalized divisia index model [16]. The factors involved in these studies include economic development, urbanization, trade openness, technological innovation, industrial structure, energy structure, and industrial green transformation, et al [2], [12]- [16]. As for technological innovation, which is also called technological progress, scholars have conducted many studies about its impact on carbon emissions from different aspects.…”

“…Since carbon emissions are not published directly in the statistical yearbook in China, we need to estimate carbon emissions adopting the calculation method of CO 2 emissions provided by the IPCC (2006). The burning of fossil fuel is the main source of CO 2 emissions, and previous studies tend to be based on energy consumption to calculate CO 2 emissions [12], [31]. Therefore, seven major fossil fuels including coal, coke, gasoline, kerosene, diesel, fuel oil and natural gas, are considered in this study to measure CO 2 emissions.…”

“…In addition, non-fossil fuels such as cement, lime, calcium carbide and steel also emit CO 2 due to physical and chemical reactions in industrial production [31]. In total emission of CO 2 from industrial production processes, cement accounts for about 56.8%, lime for about 33.7%, and calcium carbide and steel production for less than 10% [12], [31]. Data on consumption of lime, calcium carbide and steel are not available, and the share of CO 2 emissions is relatively small.…”

Application of Threshold Regression Analysis to Study the Impact of Clean Energy Development on China’s Carbon Productivity

“…This method identifies the data characteristics of unknown variables from the perspective of mathematical statistics, and determines the threshold value based on the principle of "the minimum of the squared residuals." Furthermore, the error caused by artificially dividing the threshold variable interval is avoided, and the significance test of the endogenous threshold effect is scientifically and reliably performed [15].…”

System Dynamics Model for the Evolutionary Behaviour of Government Enterprises and Consumers in China’s New Energy Vehicle Market

“…In the process of urbanisation in China, environmental governance faces enormous challenges. The government needs 2 of 21 to coordinate the interests of various entities and promote energy conservation and emission reduction via the improvement of the system [4], establishment of a green economy, and realisation of sustainable growth [5,6]. To induce the development of the electric vehicle (EV) industry in its early stage, many countries have taken action to develop economic plans and provide incentives for NEV enterprises and consumers to encourage the implementation of NEVs; among these incentives, taxes and subsidies are the most common [1].…”