An evapotranspiration deficit-based drought index to detect variability of terrestrial carbon productivity in the Middle East

The primary driver of the land carbon sink is gross primary productivity (GPP), the gross absorption of carbon dioxide (CO2) by plant photosynthesis, which currently accounts for about one-quarter of anthropogenic CO2 emissions per year. This study aimed to detect the variability of carbon productivity using the Standardized Evapotranspiration Deficit Index (SEDI). Sixteen countries in the Middle East (ME) were selected to investigate drought. To this end, the yearly GPP dataset for the study area, spanning the 35 years (1982–2017) was used. Additionally, the Global Land Evaporation Amsterdam Model (GLEAM, version 3.3a), which estimates the various components of terrestrial evapotranspiration (annual actual and potential evaporation), was used for the same period. The main findings indicated that productivity in croplands and grasslands was more sensitive to the SEDI in Syria, Iraq, and Turkey by 34, 30.5, and 29.6% of cropland area respectively, and 25 31.5 and 30.5% of grass land area. A significant positive correlation against the long-term data of the SEDI was recorded. Notably, the GPP recorded a decline of >60% during the 2008 extreme drought in the north of Iraq and the northeast of Syria, which concentrated within the agrarian ecosystem and reached a total vegetation deficit with 100% negative anomalies. The reductions of the annual GPP and anomalies from 2009 to 2012 might have resulted from the decrease in the annual SEDI at the peak 2008 extreme drought event. Ultimately, this led to a long delay in restoring the ecosystem in terms of its vegetation cover. Thus, the proposed study reported that the SEDI is more capable of capturing the GPP variability and closely linked to drought than commonly used indices. Therefore, understanding the response of ecosystem productivity to drought can facilitate the simulation of ecosystem changes under climate change projections.

China’s Carbon Emission Trading Pilot Policy and China’s Export Technical Sophistication: Based on DID Analysis

This paper takes China’s carbon emission trading pilot policy as a quasi-natural experiment, and adopts a difference-in-difference approach and data from 30 provinces in China from 2008 to 2016 to empirically study the influence of this policy on China’s export technical sophistication. The empirical analysis revealed that the policy can generate a Porter effect and progressively promote China’s export technical sophistication by reinforcing carbon productivity. By analyzing the regional heterogeneity and influence channels, the policy is found to work better in the central-western region than in the eastern region. The reason for this finding is that the policy has brought innovation offset effects to the central-western region and increased carbon productivity, but the policy has not improved carbon productivity in the eastern region. By studying the effect of three measures of policy implementation on export technical sophistication, we found that restricting carbon emission quotas distributed to participating enterprises is necessary. In addition, we found that the financial punishment method for non-performance is advantageous to the enhancement of export technical sophistication. These research conclusions can provide directions and policy recommendations for upgrading the emissions trading market, as well as a learning case and some experience for countries that have not yet established carbon trading markets.

Evaluation and Factor Analysis of Industrial Carbon Emission Efficiency Based on “Green-Technology Efficiency”—The Case of Yangtze River Basin, China

In the context of low-carbon development, effectively improving carbon emission efficiency is an inevitable requirement for achieving sustainable economic and social development. Based on panel data of 11 provinces and municipalities in the Yangtze River Basin (YRB), ranging from 2000 to 2019, this paper uses green-technology efficiency to measure industrial carbon emission efficiency via stochastic frontier analysis (SFA) incorporated with carbon productivity. This provides a comprehensive analytical framework for assessing the carbon emission efficiency, quantitatively measuring the reduction potential, and clarifying the incentive channels. The results are as follows: (1) The industrial carbon emission efficiency (ICEE) of YRB presents an increasing trend. Although differences in emission efficiency among provinces and municipalities are narrowing, their emission efficiency is still prominently imbalanced. (2) The potential for reducing industrial carbon emissions in this region shows an upward-to-downward trend. The decline in such potential of each province and municipality in recent years indicates that further reduction is becoming more difficult. (3) Effective means to improve ICEE are to improve the level of industrialization, promote technological innovation in industrial low-carbonization, and raise industrial productivity. Meanwhile, the significant spatial spillover effect of ICEE further emphasizes the necessity of strengthening the coordination of carbon reduction policies in YRB. The research in this paper adds a new perspective to the evaluation of ICEE and also provides reference and technical support for the government to enhance ICEE and formulate green and sustainable development policies.

Do environmental policy and innovation improve carbon productivity? Evidence from the Korean Emission Trading Scheme

This study investigates the relationship among technology innovation, emission trading schemes, and carbon productivity based on data from firms participating in the Korean Emission Trading Scheme. First, the total factor carbon productivity based on stochastic frontier analysis is estimated by industry and it is confirmed that changes in carbon productivity vary by industry. Based on the estimated carbon productivity, panel data analysis is conducted to determine the effects of innovation and environmental policy on carbon productivity. The results show that R&D investment and environmental policy play an important role in promoting carbon productivity. In this study, the factors affecting carbon productivity are also analyzed by industry. Comparative analysis across industries confirms that factors affecting environmental performance vary by industry. Innovation does not significantly affect carbon productivity in assembling industries, but in the process industry, R&D investment plays an important role in increasing environmental performance.

The Spatial Spillover Effect of Environmental Regulation and Technological Innovation on Industrial Carbon Productivity in China: A Two-Dimensional Structural Heterogeneity Analysis

Environmental regulation and technological innovation are two crucial factors for improving industrial carbon productivity. However, prior research ignored the spatial spillover effects of these factors, and heterogeneity caused by industrialization level and resource dependence did not acquire attention either. Thus, we use the STIRPAT model and spatial panel Durbin model to study the spatial spillover effects of two independent variables. Then, a two-dimensional structural heterogeneity analysis is conducted according to the industrialization level and resource dependence. The results are as follows: improving environmental regulation and technological innovation is good for industrial carbon productivity. Simultaneously, there are obvious regional differences under two-dimensional structural heterogeneity. From the perspective of space, industrial carbon productivity has high spatial autocorrelation, and it can be enhanced through local environmental legislation, as well as technological innovation. Environmental regulation’s spatial spillover impact inhibits the improvement of industrial carbon productivity in surrounding provinces, resulting in a pollution haven effect. However, there is no evident regional spillover effect of technological innovation. Therefore, we provided new perspectives from spatial spillover and structural heterogeneity to optimize low-carbon policies.

Research on Carbon Productivity and It's Spatial Convergence of Steel Industry in China

The Global-Malmquist-Luenberger (GML) index was applied to analyse the carbon productivity in steel industry (SICP) of 29 provinces in China from 2006 to 2017, and then the SICP was decomposed into technical efficiency change index (TC) and technical progress index (EC). On this basis, the spatial effect is introduced into the traditional convergence model to investigate the spatial convergence of SICP. The empirical results show that: (1) The overall carbon productivity of China's steel industry is at a relatively low level, showing a slow growth trend. (2) The average value of the GML index of SICP is higher than 1, showing obvious inter-provincial and regional heterogeneity. Compared with EC, TC is the leading factor that promotes the increase of SICP. (3) The spatial absolute and condition β convergence of SICP exist in the whole country and the three major regions, but the σ convergence feature is not significant. The addition of spatial factors speeds up the convergence trend, and the speed of spatial absolute β convergence is about 3 times that of the classical convergence model. At the same time, the conditional convergence rate is significantly faster than the absolute convergence, which is closely related to the differences in influencing factors such as the industrial structure, economic development level, human capital, energy consumption intensity, and R&D investment among regions. There is still much room for improvement in carbon productivity in China's steel industry, and investment in scientific research must be increased in order to achieve the upgrading of the industrial structure and technological innovation. The existence of spatial convergence requires strengthening the joint reorganization of steel enterprises between provinces and regions, making full use of the spatial spillover effects of production technology, and realizing regional green and coordinated development.

Driving Factors and Growth Potential of Provincial Carbon Productivity in China

Climate change has become a global concern, and the development of a green economy has attracted wide attention. Understanding the driving factors and growth potential of provincial-level carbon productivity is crucial for China’s green economic development in the new normal phase. In this study, the logarithmic mean Divisia index (LMDI) is adopted to systematically investigate the driving factors of provincial carbon productivity and explore the growth potential of provinces’ carbon productivity based on the clustering analysis. The results show that: (1) China’s provincial carbon productivity presents an increasing trend in 2001–2017, but the differences in carbon productivity among provinces are widening. (2) Economic activity and industrial structure are key to push up regional carbon productivity in China, while energy intensity is the main factor pulling it down. (3) The potential for carbon productivity improvement varies greatly among provinces in the four groups. Specifically, in groups 1 and 2, the developed provinces have little potential for improving carbon productivity, while the developing provinces in group 4 are just the opposite. These findings can enlighten policymakers that the development of a green economy should focus on optimizing and upgrading industrial structure and reducing energy intensity, and provincial heterogeneity must be considered when formulating green economic development policies.