Economics and Information Theory

This book opens a fresh chapter in the debate on local enterprise clusters and their strategies for upgrading in the global economy. The authors employ a novel conceptual framework in their research on industrial clusters in Europe, Latin America and Asia and provide new perspectives and insights for researchers and policymakers alike.

Governance in global value chains

We focus on three environmental impacts particularly influenced by population age-structure—carbon emissions from transport and residential energy and electricity consumption—as well as aggregate carbon emissions for a panel of developed countries, and take as our starting point the STIRPAT framework. Among our contributions is to further disaggregate population into three particularly key age groups: 20-34, 35-49, and 50-64, and by doing so demonstrate that population’s environmental impact differs considerably across age-groups, with the older age-groups (ones typically associated with larger households) actually exerting a negative influence. Furthermore, those age-specific population influences are different (in absolute and relative terms) for the different environmental impacts we analyze. Also, we find that urbanization, in developed countries, best measures access to a country’s power grid, and thus, is positively associated with energy consumption in the residential sector. Lastly, we suggest some modelling and methodological improvements to the STIRPAT framework.

Age-Structure, Urbanization, and Climate Change in Developed Countries: Revisiting STIRPAT for Disaggregated Population and Consumption-Related Environmental Impacts

How do environmental regulation and environmental decentralization affect green total factor energy efficiency: Evidence from China

Green growth and low carbon emission in G7 countries: How critical the network of environmental taxes, renewable energy and human capital is?

This paper uses dynamic spatial panel models to analyze the effects of industrial structure and technical progress on carbon intensity in order to explore those factors that may lead to a reduction in carbon intensity in China. The results show that there is both significant positive spatial autocorrelation and spatial heterogeneity between provinces in carbon intensity. Although upgrading and optimization of industrial structure are conducive to reducing carbon intensity, in China it is technical progress that plays the most important role. Efficiency change is a leading factor in the reduction of carbon intensity by technical progress, but the effect of technical change is not significant because of the carbon emissions rebound effect. Although technical change itself cannot directly reduce carbon intensity, it can indirectly do so by promoting the upgrading and optimization of industrial structure. Both structure of urbanization and coal-dominated energy consumption increase carbon intensity but the relationship between foreign direct investment and carbon intensity is not significant. Based on these empirical findings, we put forward specific suggestions for the Chinese government in their quest to reduce carbon intensity.

Industrial structure, technical progress and carbon intensity in China's provinces

Identifying the spatial effects and driving factors of urban PM2.5 pollution in China

The emissions reduction effect and technical progress effect of environmental regulation policy tools

An improvement in industrial carbon emission efficiency is crucial for achieving both reductions in carbon emissions and sustainable economic growth. In this paper, we use an improved non-radial directional distance function (NDDF) to construct a new meta-frontier total-factor carbon emission efficiency index (TCEI) with which we estimate the meta-frontier TCEI of China's 30 provincial industrial sectors in 2005–2015 and analyze their dynamic evolution. The results show that compared to traditional NDDF, the improved NDDF has more advantages in measuring both carbon emission efficiency and the technology gap ratio. For the study period, China's industrial meta-frontier TCEI is low, indicating that the industrial TCEI of many provinces still has much room for improvement. The meta-frontier TCEI has significant inter-group heterogeneity, with Eastern China having the largest carbon emission efficiency, followed by Central China, and Western China having the lowest. China's industrial meta-frontier TCEI increased significantly during the study period with technical progress playing a major role in promoting it. Over time, however, the meta-frontier TCEI growth rate decreased significantly as the deterioration in technological efficiency and the expansion of the technology gap have jointly inhibited the growth of carbon emissions efficiency. Carbon emission performance in various regions over different periods exhibit differing characteristics, that is, the carbon emission performance has significant spatial heterogeneity and period heterogeneity.

Zhonghua Cheng

Papers

Industrial structure, technical progress and carbon intensity in China's provinces

Identifying the spatial effects and driving factors of urban PM2.5 pollution in China

The emissions reduction effect and technical progress effect of environmental regulation policy tools

Total-factor carbon emission efficiency of China's provincial industrial sector and its dynamic evolution

Does industrial agglomeration promote the increase of energy efficiency in China