Chinese Journal of Population Resources and Environment最新文献

Dual control of energy consumption is an effective method proposed in China to launch and deepen the transition in energy use. It has obtained favorable results; however, the resource consumption and pollution emissions per unit of Gross Domestic Product (GDP) remain high. This article quantitatively studies the current policies’ effects to promote the advancement of dual control of energy consumption and develop future policies. Green production and consumption policies promote the development of dual control of energy consumption; therefore, this paper examines these policies through text mining and a quantitative evaluation system. The results suggest strong state demand for developing green production and consumption and the necessity for scientific evaluation; however, the state’s focus on the relevant policy tools is imbalanced. Constraint policy tools have the highest proportion of restricting pollutant emissions but lack comprehensiveness. Incentive and guidance policy tools have the problems of poor targets and low volume. Additionally, by constructing policy modeling consistency (PMC) index models for evaluating green production and consumption policies, six selected policies indicate solid performance, but some still have problems regarding policy timeframe, policy function, and green process. Based on the conclusions, this paper provides some targeted recommendations.

The rapid development of the digital economy provides an unprecedented opportunity for China to achieve carbon neutrality by 2060. While previous studies have explored the relationship between the digital economy, digital technologies, and energy, the impact of the digital economy on carbon emissions has not received sufficient attention in the literature. Meanwhile, although cities are the basic units for carbon emission reduction policies, few studies have explored carbon emissions at the city level in China. This study investigates the spatial correlation and spillover effects of the digital economy development on carbon emission reduction in 248 prefecture-level cities in China from 2011 to 2019. The proposed approach relies on Moran’s I test and the spatial Durbin model (SDM). First, the study’s results show that carbon emissions are more severe in industry-intensive cities in northeast and central China than in the eastern coast during the sample period. Second, the development of the digital economy is conducive to reducing carbon emissions. Third, Moran’s I and SDM tests find that the digital economy has a significant spatial effect on carbon emissions, with a close spatial connection between cities. Based on the above findings, this study offers relevant policy recommendations and suggestions for realizing China's carbon neutrality goal.

In the era of large-scale retirement of power batteries, there are information barriers and high recovery costs in their recycling. In view of this, in this study we constructed a tripartite evolutionary game model of the cooperation between power battery production and recycling enterprises and government participation. We analyzed the strategic choice of the three parties in the process of power battery recycling and simulated the influence of participants' willingness and information barriers on the strategic choices of the parties. The results showed that power battery production and recycling enterprises, and the government are affected by each other's willingness to participate at different degrees. The willingness of power battery manufacturers and recycling enterprises to cooperate with each other decreased with increases in information barriers.By analyzing the impact of information barrier on power battery recycling, some suggestions are put forward to provide decision-making reference for promoting the sustainable development of power battery industry.

Human capital is an important aspect of energy consumption, exerting crucial effects on economic growth, technological progress, and economic restructuring. This paper presents an in-depth investigation of the effect of human capital on energy consumption using an extended version of the Stochastic Impacts by Regression on Population, Affluence, and Technology framework. The estimated results using a panel dataset covering China’s 30 provincial regions during the period 1997–2018 and applying fixed effects with instrumental variables and the generalized method of moments indicated that an increase in human capital significantly drove energy consumption. A 1% increase in human capital increased energy consumption by approximately 0.3%. A two-step channel analysis to test scale, technical, and structural effects revealed that the positive effect of human capital on energy consumption is based primarily on the scale effect. However, highly educated human capital alleviates the energy pressure of this effect. In contrast to the scale effect, both the technical and structural effects of human capital reduced energy consumption, and this reduction is primarily correlated with enterprises’ utility-oriented technological progress. Finally, we present strategic energy control policy implications related to human capital.

Air pollution, especially fine particulate matter (PM_2.5) pollution, has a great negative impact on human production and normal life. It is a hot issue of global concern, and it is also a huge test for China’s high-quality socioeconomic development. Environmental absorption capacity (EAC) is the guarantee of the structure and function of a healthy atmospheric environmental system. It plays an important role in the automatic accommodation, absorption, and digestion of air pollutants produced by humans in the process of industrialization and civilization. Understanding the absorption capacity of environmental elements on PM_2.5, and exploring the impact of EAC on PM_2.5 concentration are of great significance to the development of new ideas for air pollution control. This paper constructed an index system to calculate the EAC index from two dimensions (natural resource endowment and impact of human activities), analyzed and discussed the impact of EAC on PM_2.5 concentration and its heterogeneity effect through panel regression model, generalized panel quantile regression based on MCMC optimization and scenario analysis, by using the panel data of 30 provinces in China from 2004 to 2017. The results suggested that: (i) The overall level of national EAC was low, and the regional differences were large. EAC was influenced by natural resource endowment and human activities, while natural endowment was the main factor affecting EAC, and human activities were the main factor causing the fluctuation and the regional difference of EAC. (ii) As the result of full sample regression demonstrated, the enhancement of EAC had a significant negative influence on the reduction of PM_2.5 concentration. In areas with high PM_2.5 concentration, the effect of EAC was more obvious, but the effect did not necessarily increase with the increase of PM_2.5 concentration. Also, in extreme cases, the effect did not pass the significance test. (iii) According to the results of heterogeneity effect analysis, the effect of EAC on PM_2.5 concentration was significantly negative in sustainable scenario and emergency scenario, while the effect was not significant in Soylent Green scenario. The conclusion provides important implications for the prevention and control of air pollution and the improvement of atmospheric environmental quality.

So far there is a lack of specialized research on the entire path and the law of the evolution of green economy modes, especially studies on its future trend. Some studies have been done only on its sub-process, in which there are some shortcomings such as overlapping and incomplete classification of economy modes, as well as the lack of mechanism models to describe the modes. This paper attempts to solve those problems. Firstly, various green economy modes are extracted and classified, and then their evolution paths are identified and reviewed, according to sustainable development theory, hypercycle theory, etc., and to practical investigation. Secondly, theoretical models for each green economy mode and their multilevel dynamic evolution models in a practical field (forestry as an example) are established by the modeling method of system structure. Finally, the evolution law of green economy mode is analyzed systematically by means of the above-mentioned models. According to the research, some conclusions are drawn as follows: First, green economy modes have evolved through three stages as a whole. The first is the start-up stage, in which the mode is the ‘from cradle to grave’ end-treatment green economy (GE-I). The second is the growth stage, in which the mode is the ‘from cradle to cradle’ resources-chain closed-loop green economy (GE-Ⅱ). The third is the mature stage, in which the mode will be the ‘from breeding to breeding ‘three-chain hypercycle economy (GE-Ⅲ). Second, GE-Ⅱ has evolved through a specific process. In depth, the basic framework of GE-Ⅱ is based on the circular economy theory; and then it is combined gradually with some other relevant theories and technologies that are cleaner production, low-carbon economy and sharing economy and so on. In breadth, GE-Ⅱ is expanded layer by layer from unified enterprise GE-Ⅱ to diversified enterprise ( or cluster) GE-Ⅱ, and to societal GE-Ⅱ. The operational principle of GE-Ⅱ can be described by 3R theoretical model that includes 3 subsystems of reduction, recycle and reuse (3R), which are connected into an organic whole by resources chain. Third, GE-Ⅲ is evolving through a specific process of three-chain (3C) stage by stage expansion, from resources-chain primary hypercycle to eco-chain secondary hypercycle, and to value-chain tertiary hypercycle. The operational principle of GE-Ⅲ can be described by 5R-3C theoretical model that includes 5 subsystems of reduction, recycle, reuse, recultivation and reallocation (5R) , which are connected into an environment-economy self-organizing system by resources chain, ecochain and value chain. In the system, resources chain is the foundation, eco-chain is the support, and value chain is the impetus. The 3C hypercycle promotes and catalyze each other to realize mutualism between ecosystem and industrial system. Fourth, the related field of GE-Ⅲ includes the principal part of renewable-resource-based industry, and the expanded part of other relev