Does government environmental attention drive green total factor productivity? Evidence from China.

Based on city-level panel data spanning 2008 to 2021, this study investigates the impact of government environmental attention (GEA) on green total factor productivity (GTFP). The findings suggest that increased GEA substantially enhances the growth of GTFP. After conducting robustness and endogeneity tests, the study's results consistently show reliability and robustness. Further analysis elucidates several mechanisms through which GEA influences GTFP, including fostering green technology innovation, optimizing resource allocation, and promoting upgrades in industrial structure. Heterogeneity analyses reveal that the impact of GEA on GTFP is notably pronounced in eastern cities, as well as in cities characterized by low resource dependency, mature industrial development, and high market competition. Conversely, the influence of GEA on GTFP is less discernible in cities prioritizing economic development goals, possibly due to differing policy orientations and resource allocation strategies. This study offers a novel perspective on understanding how GEA shape green development and provides empirical support for policy formulation.

What drives the export-related carbon intensity changes in China? Empirical analyses from temporal-spatial-industrial perspectives.

This study aims to explore the driving determinants on the export-related carbon intensity (ECI) of China, to better understand the impact of international trade on climate change governance and facilitate China's carbon intensity mitigation goals. First, China's ECI evolution and its gaps with the USA and India are measured during 2002-2014. Then, the main drivers of China's ECIvert study further discusses the influencing factors of ECI in the manufacturing industry using the environmental-extended STIRPAT model and GMM method. The results show that (1) China's overall ECI increases from 1.50 Kg/US$ in 2002 to 1.92 Kg/US$ in 2005 and then decreases to 1.27 Kg/US$ in 2014. The ECI of the manufacturing industry is significantly higher than that of the agriculture and service industry. China's ECI gap with the USA is greater than that with India, and both show a downward trend. (2) Carbon emission coefficient is the domain factor to reduce China's ECI during 2002-2014; the effects of the value-added coefficient, input-output structure, and final demand are limited. The input structure dominantly expands China's ECI gaps both with the USA and India, followed by the value-added coefficient. The carbon emission coefficient enlarges the ECI gap with the USA while reduces that with India. (3) Industrial productivity and value-added rate are negatively correlated with ECI in the manufacturing industry, while per capita capital stock plays the opposite role. The positive correlation between energy intensity and CIE becomes significant after distinguishing technology heterogeneity. In contrast to the non-tech-intensive manufacturing industry, the increase of backward GVCs participation of tech-intensive ones will reduce the ECI. The threshold effect of backward GVCs participation exists in the whole manufacturing industry. Targeted ECI reduction policy implications are suggested.

Global value chains participation and carbon emissions embodied in exports of China: Perspective of firm heterogeneity.

This paper studies the impact of global value chains (GVCs) participation on carbon emissions embodied in exports (EEE) of China during 2005-2016, and analyses firm heterogeneity from the perspective of firm ownership and trade patterns; Then, industries are classified based on factor intensity and technology level, and the industrial heterogeneity of different firms is analyzed; Finally, through counterfactual analysis, this paper evaluates EEE under four anti-globalization scenarios. The empirical study shows that with the increase of GVCs position, EEE first increases and then decreases, showing an inverted U-shaped distribution. From the perspective of firm ownership, the relationship between GVCs position of domestic firms and EEE is inverted U-shaped distribution, while GVCs position of multinational enterprises (MNEs) is positively correlated with EEE. The relationship of capital-intensive and technology-intensive domestic firms, high-tech and medium-tech manufacturing domestic firms are also inverted U-shaped distribution. The positive correlation of MNEs is reflected in the low-tech manufacturing industries. From the perspective of trade patterns, with the increase of GVCs position, EEE of processing trade firms presents an inverted U-shaped distribution, while it is not significant for general trade firms. The inverted U-shaped relationship between GVCs position in capital-intensive industries and EEE are confirmed in all firms. Under anti-globalization scenarios of 50% backflow, EEE would increase. Under scenarios of 100% backflow, EEE would reduce. The results above provide useful references to achieve carbon emission reduction targets.

How China's electricity generation sector can achieve its carbon intensity reduction targets?

As the largest sector with decarbonization potential, electricity generation is critical for achieving carbon intensity reduction targets of China by 2020 and 2030. This study combines temporal decomposition and scenario analysis to identify the key drivers and provinces with increasing carbon intensity of electricity generation (CIE) and designs four scenarios by integrating efficiency improvement and structural adjustment in 30 provinces of China, and estimates the possible reduction of CIE by 2020 and 2030. Results show that 1) CIE in China decreases by 7.25% during 2001-2015. The estimated CIE during 12th FYP in this study is 25% lower than the estimation using IPCC emission factors, which is closer to China's reality. 2) Driving forces of CIE changes in 30 provinces vary greatly across provinces. The increasing CIE in four worse-performance regions (i.e. Northeast, South Coast, Southwest, Northwest) is mainly caused by energy mix effect and geographic distribution effect. The CIE growth in South Coast is also related to thermal power share effect. 3) Both 2020/2030 targets can be achieved by regulating the drivers for CIE growth in 30 provinces (i.e., RAK scenario). CIE decline is concentrated in three types of provinces, namely provinces with large economic size, strong policy support and clean energy implementation. The findings and recommendations provide insights into achieving 2020/2030 targets for CIE reduction.

Scenario analysis of ETS revenue allocation mechanism of China: based on a dynamic CGE model.

The successful establishment of China's emission trading scheme (ETS) could lead the next generation of global climate carbon markets in industrializing and developing countries. The allocation of ETS revenue from auctioning carbon emission allowance is important for the achievement of China's joint targets of economic growth, mitigation, and welfare improvement. This study develops a dynamic CGE model to evaluate the effects of different ETS revenue allocation mechanisms and identifies the proper mechanism for China's ETS design. Ten scenarios including business as usual (BAU), no ETS revenue allocation incentive (NA) and other eight ETS revenue allocation scenarios are designed. Simulation results indicate that the tradeoff between economic cost and environmental benefit exists under different ETS revenue allocation mechanisms. ETS revenue is suggested to allocate to household sector through reducing indirect tax and, after 2020, a certain proportion of ETS revenue could be allocated to production sector for improving energy-saving technology (i.e., STP mechanism). This study provides references for policymakers in China to design effective and realistic ETS-related policies. A similar study could be conducted to explore the proper ETS and the revenue allocation policies in other countries that have similar national conditions to China, such as other BRICS countries.

PLD1 overexpression promotes invasion and migration and function as a risk factor for Chinese glioma patients.

Glioma is a lethal disease with few effective therapeutic options. Recently, insights into cancer biology had suggested that abnormal lipid metabolism was a risk factor for various human malignancies, including glioma. As a key enzyme implicated in lipid metabolism, PLD1 was overexpression in multiple human cancers, and it was stated to be responsible for aggressive phenotypes, such as angiogenesis and chemoresistance. However, there was still much to know about its expression and function in glioma. In the present study, we showed that PLD1 was overexpression in clinical samples of glioma. In addition, the correlation assay revealed that PLD1 overexpression was correlated with poor differentiation (p = 0.04), and it was responsible for a poor prognosis for the patients (p = 0.009). Furthermore, we showed in COX regression assay that PLD1 was a risk factor for glioma (p = 0.018, HR = 0.461, 95% CI = 0.243-0.887). Consistently, we found that PLD1 was overexpression in glioma cell lines, and it could facilitate the proliferation and migration. Taken together, our study suggested that PLD1 was pro-tumoral in glioma, and that further studies were urgently needed so as to define whether it was a novel therapeutic target for the disease.