Since the industry is economic backbone and the largest sector in energy-related CO2 emissions, whether it can coordinate industrial growth and CO2 emissions plays a vital role in achieving economic ...sustainable development. Using the global Malmquist–Luenberger (GML) index method, this paper estimates and decomposes the total factor CO2 emission performance (TFCEP, i.e., the environmentally sensitive productivity growth considering CO2 emissions as an undesirable output) of 32 industrial sub-sectors in Shanghai (China) over 1994–2011 for the first time. Furthermore, it adopts the system generalized method of moments (SGMM) to investigate the determinants of the TFCEP. We find that the environmentally sensitive productivity of overall industry in Shanghai keeps improved in recent years. Technical progress rather than efficiency promotion is the main contributor to ameliorate the TFCEP. Enhancing R&D intensity, optimizing energy consumption structure, and improving energy efficiency and labor productivity are beneficial to enhance the TFCEP, while capital deepening is the detriment of the TFCEP. Encouraging green technological innovation and adoption by combining the government intervention with market mechanism is significant to promote the TFCEP.
•We investigate Shanghai's industrial total factor CO2 emission performance (TFCEP).•We use the global Malmquist–Luenberger index method to estimate the TECEP.•We adopt the generalized method of moments to examine the determinants of the TECEP.•Technical progress is the main contributor to improve the TFCEP.•Motivating green technological innovation and adoption is vital to promote the TFCEP.
Green innovation has been positioned as an effective way to balance economic development and environmental governance. However, the impact of green innovation (i.e., innovation relating to the ...environmentally sound technologies (ESTs)) on carbon emission performance in a large developing country, such as China, has been paid little attention. This paper investigates the impact of green innovation on carbon emission performance based on a panel data set covering 218 prefecture-level cities in China from 2007 to 2013. First, we examine whether heterogeneous green innovations have a synergistic effect on carbon emission performance using the two-way fixed effect model, instrumental variable method, and spatial econometric model. Moreover, using a causal mediation effect model, we identify four kinds of potential transmission channels of green innovation affecting carbon emission performance: energy consumption structure effect, industrial structure effect, urbanization effect, and foreign direct investment (FDI) effect. The results indicate a positive effect of green innovation and its sub-categories on carbon emission performance in China. However, a noteworthy phenomenon is that direct carbon emission-reduction innovation and green administrative innovation have a weaker effect on carbon emission performance than other kinds of green innovations. In addition, the positive effect has an evident heterogeneity in different kinds of cities. To be specific, green innovation has an evident positive impact on carbon emission performance in key cities for environmental protection, resource-based cities, non-resource-based cities, and central cities. Meanwhile, a “snowball” effect and a symbiotic effect of carbon emission performance exist in local cities and between cities, respectively. Finally, we find that green innovation significantly decreases and increases carbon emission performance through industrial structure effect and FDI effect, respectively.
•We investigate the impact of heterogeneous green innovations on carbon emission performance in China.•We use a panel data set covering China's 218 prefecture-level cities over 2007–2013.•Instrumental variable method and spatial econometric model are employed.•We find a positive effect of heterogeneous green innovations on carbon emission performance in China.•Green innovation improves carbon emission performance through some mediation effects.
Aims
The differential diagnosis between ALK-negative anaplastic large cell lymphoma (ALK
-
ALCL) and peripheral T-cell lymphoma, not otherwise specified (PTCL, NOS) with high expression of CD30 (CD30
...high
) are essential. However, no reliable biomarker is available in daily practice except CD30. STAT3 is characteristically activated in ALCL. We aimed to investigate whether the status of STAT3 phosphorylation could help the differential diagnosis.
Methods
The status of phosphorylation of STAT3 was examined using two antibodies against pSTAT3-Y705 and pSTAT3-S727 by immunohistochemistry in ALK
+
ALCL (n=33), ALK
-
ALCL (n=22) and PTCL, NOS (n=34). Ten PTCL, NOS with diffuse CD30 expression were defined as CD30
high
PTCL, NOS. Flowcytometric analysis were performed to evaluate the expression of pSTAT3-Y705/S727 in PTCL, NOS (n=3).
Results
The median H-scores of pSTAT3-Y705 and S727 were 280 and 260 in ALK
+
ALCL, 250 and 240 in ALK
-
ALCL, and 45 and 75 in CD30
high
subgroup, respectively. Using H score of 145 as the cutoff value, pSTAT3-S727 alone distinguished between ALK
-
ALCL and CD30
high
PTCL, NOS with a sensitivity of 100% and specificity of 83%. Additionally, pSTAT3-S727, but not pSTAT3-Y705, was also expressed by background tumor-infiltrating lymphocytes (S727
TILs
) in PTCL, NOS. PTCL, NOS patients with high S727
TILs
H score had a favorable prognosis than those with no TILs (3-year OS rate: 43% vs. 0,
p
=0.013) or low S727
TILs
(3-year OS rate: 43% vs. 0,
p
=0.099). Flowcytometric analysis revealed that of the three patients investigated, two had enhanced pSTAT-S727 signals in neoplastic cell populations, and all three patients were negative for pSTAT3-Y705 expression in both tumor cells and background lymphocytes.
Conclusions
pSTAT3-Y705/S727 can be used to help distinguish ALK
-
ALCL from CD30
high
PTCL, NOS and pSTAT3-S727 expression by TILs predicts the prognosis of a subset of PTCL, NOS.
Whether or not carbon regulation policies can achieve the “double dividend” of carbon reduction and economic growth is vital for realizing the sustainable development of a certain country. This paper ...investigates the effects of a carbon intensity constraint policy (CICP) that the Chinese government put forward in 2009 on the green production performance (GPP, i.e., the environmentally sensitive productivity growth considering carbon emissions to be an undesirable output) of industrial sector (the largest carbon emitter in China) for the first time. Based on a non-radial and non-oriented DEA (data envelopment analysis) measure method, we first adopt the Luenberger indicator to estimate the GPP of China's 36 industrial sub-sectors over 2001–2013. Furthermore, regarding the CICP proposed in 2009 as a natural experiment, we assess the effects of such a policy on China's industrial GPP by using the quasi-difference-in-differences (quasi-DID) method. The results show that China's industrial GPP presents a circuitous downward trend after experiencing a transient rise. The heterogeneity of the GPP among industrial sub-sectors exists, and the increase in industrial output is the crucial driver of improving the GPP. China's industrial GPP has deteriorated after implementing the CICP, and the negative effect of such a policy is larger and larger over time. Such empirical results indicate that although the carbon regulation policy in China has achieved a surface success, the policy causes a factor substitution effect to hinder the improvement of the GPP. Therefore, China's current CICP is not effective in realizing the “double dividend” of carbon reduction and industrial growth.
•We measure the green production performance (GPP) of China's industrial sub-sectors.•We explore the effect of China's carbon intensity constraint policy (CICP) on GPP.•The industrial GPP in China has deteriorated after implementing the CICP in 2009.•The negative effect of the CICP on the GPP becomes larger and larger over time.•The CICP causes a factor substitution effect to hinder the improvement of the GPP.
With the increasing concerns of global economic recovery and climate change, the improvement of carbon emission efficiency has become extremely significant to get rid of economic and environmental ...dilemmas. Although natural resource development is usually carbon-intensive, little attention has been paid to the impact of natural resource dependence on the carbon emission efficiency. Based on the resource curse theory and the stylized facts of resource-based regions in China, for the first time, this paper proposes the hypothesis of the carbon emission efficiency curse. The hypothesis suggests that an increase of natural resource dependence can suppress the carbon emission efficiency by crowding out green technological innovation, curbing the carbon productivity of investment, and reducing population density. Furthermore, we verify the hypothesis using the instrumental variable approach and the panel data of 283 cities in China from 2004 to 2017. The results demonstrate the negative effect of natural resource dependence on the carbon emission efficiency. This finding remains robust after replacing the indicators of the key variables, altering the estimation method, and eliminating the disturbances resulting from particular samples and the low-carbon policy. The mechanism analysis indicates that natural resource dependence can crowd out green technological innovation, restrain the carbon productivity of investment, and lower population density to curb the improvement of the carbon emission efficiency. In addition, the heterogeneity analysis shows that although the carbon emission efficiency curse exists significantly in resource-based cities on average, renewable resource-based cities successfully avoid the carbon emission efficiency curse because of the optimization and transformation of economic development mode. Meanwhile, the negative impact of natural resource dependence on the carbon emission efficiency is not significant in the Two Control Zone cities and key cities for air pollution prevention and control, implying that environmental regulations are conducive to avoiding the carbon emission efficiency curse. This paper provides a novel perspective for the carbon emission efficiency improvement and low-carbon transition of China and other developing countries.
•We propose and examine the hypothesis of the carbon emission efficiency curse.•We use a panel data set of China's 283 cities and the instrumental variable approach.•We confirm the negative effect of natural resource dependence on the carbon emission efficiency.•Renewable resource-based cities successfully avoid the carbon emission efficiency curse.•Environmental regulations are conducive to avoiding the carbon emission efficiency curse.
With the increasing concerns of global climate change, clean energy development is regarded as one of the most important measures to mitigate CO2 emissions. However, existing studies pay little ...attention to the spatial spillover effect of clean energy development on CO2 emissions. Using a provincial-level panel data set during 1997–2017 and a spatial Durbin model, this is the first study to investigate the effect of clean energy development measured by the share of clean energy generation in total electricity generation on CO2 emissions in China. The results show that clean energy development causes less CO2 emissions in the local region but more CO2 emissions in spatially related regions. This finding is reinforced through a series of robustness checks. The heterogeneity analysis indicates that the local CO2 emission reduction effect of clean energy development is greater in the period of 2013–2017, electricity-poor regions, and low-carbon pilot regions; meanwhile, electricity-poor regions and low-carbon pilot regions suffer from a more adverse impact of clean energy development in spatially related regions on local CO2 emission reduction in the long run. Furthermore, the results of mechanism analysis suggest that the fossil energy saved by the usage of clean energy from the power generation sector in the local region flows into spatially related regions to crowd out clean energy consumption in these regions. Therefore, the overall effectiveness of CO2 emission reduction effort in clean energy development is partly undermined by the CO2 transfer effect. This paper provides a novel perspective to understand the effect of clean energy development on CO2 emission reduction and helps to promote inter-regional cooperative CO2 emission reduction within a country.
•We investigate the effect of clean energy development on CO2 emissions in China.•We use the spatial Durbin model based on a provincial panel data set over 1997–2017.•A rise in local clean energy development causes more CO2 emissions in neighbor regions.•Fossil energy saved by clean energy use in the local region flows into neighbor regions.•Emission reduction effort in clean energy development is offset by CO2 transfer effect.
•We investigate the effect of income on residents’ WTP for environmental protection.•We use ordered Logit model and micro data from the Chinese General Social Survey.•We find that the WTP does not ...always rise with the increase in residents’ income.•The marginal WTP for environmental protection declines with the increase in income.•The residents in more polluted cities have higher WTP for environmental protection.
The majority of existing studies argue that rich people and the residents in high-income countries and regions have stronger willingness to pay (WTP) for environmental protection. Does such a rule hold true for China at the present stage? Previous studies pay little attention to this issue due to the lack of related data. Merging the micro data from the Chinese General Social Survey in 2010 (CGSS2010) with the macro data at the corresponding urban level of China, as well as two types of satellite monitoring data, this paper investigates the effect of income on residents’ WTP for environmental protection at both macro and micro perspectives based on the ordered Logit model. The results show that the rich do have stronger WTP for environmental protection. However, with the increase in residents’ income, the marginal WTP for environmental protection will decline, and a reversal occurs at the top income level. Therefore, the WTP does not always rise with the increase in income, and the middle-income class has the strongest WTP for environmental protection. Moreover, after controlling individual characteristics, residents’ WTP for environmental protection more depends on environmental pollution degree rather than urban average income level measured by both GDP per capita and the nighttime lights data from satellite monitoring. The residents in more polluted cities have stronger WTP for environmental protection. Therefore, it is not reasonable to improve people’s environmental preferences purely through economic development.
Based on panel data of listed companies in China from 2006 to 2020, this study takes the establishment of automatic air quality monitoring stations as a quasi-natural experiment and uses the ...staggered difference-in-differences method to explore whether the establishment of monitoring stations promotes green innovation of listed companies. The empirical results show that: (1) The green innovation of companies achieves an increase of 3.5% with monitoring stations in their locations, and an increase of 2.3% with the establishment of each additional monitoring station. This conclusion is valid after a series of robustness tests and exclusive tests. (2) The heterogeneity analyses show that monitoring stations have a greater role in promoting green innovation for non-state-owned enterprises, enterprises in heavy polluting industries and enterprises in key cities for environmental protection. (3) The transmission mechanism test results show that the establishment of automatic air monitoring station has crowding-out effect rather than leverage effect on green innovation, substantial innovation rather than strategic innovation. (4) The further analyses manifest the promotion of end-to-end green innovation, independent invention and quality of green patents.
•Establishing automatic air monitoring station significantly promotes the company's green innovation.•There are different effects on green innovation, due to ownership, pollution intensity and regulatory pressure.•Establishing monitoring station affects green innovation, displaying crowding-out effect and substantial innovation.•Establishing monitoring station can promote independent invention, end-to-end green innovation and quality of green patents.
Using a panel data set of 248 Chinese cities at the prefecture level and above from 2004 to 2013, this study employs the data envelopment analysis (DEA) method based on a non-angular and non-radial ...directional distance function (DDF) combined with the overall technology, to measure the haze-governance performance. Furthermore, we construct a composite index based on the nighttime light (NTL) data to reflect the urbanization level, and use a spatial Durbin model (SDM) to investigate the effect and its mechanism of urbanization on the haze-governance performance. The results show a significant U-shaped curve relationship between urbanization and haze-governance performance for the samples of both the whole country and sub-regions. When urbanization exceeds a certain critical level, urbanization is conducive to the improvement of haze-governance performance. The proportion of cities exceeding the critical level in eastern China is higher than in central and western China. The mechanism analysis reveals that urbanization exerts a U-shaped influence on haze-governance performance via the effects of industrial structure, technological innovation, and human capital accumulation. In addition, as for the whole country, urbanization in neighboring regions also has a U-shaped spatial spillover effect on local haze-governance performance; however, the corresponding critical value is relatively small. In eastern China and in central and western China, urbanization in neighboring regions exhibits one-way positive and negative effects on local haze-governance performance, respectively.
•We use a data envelopment analysis model to measure China's haze-governance performance.•We use a spatial Durbin model to explore urbanization's effect on haze-governance performance.•A U-shaped curve relationship between urbanization and haze-governance performance exists.•Urbanization affects haze-governance performance through three channels.•Neighboring urbanization has a U-shaped spatial spillover effect on local haze-governance performance.
