Strategic interaction of environmental regulation and green productivity growth in China: Green innovation or pollution refuge?

This study measures and decomposes green productivity growth of Korean manufacturing industries between 2004 and 2010 using the Malmquist-Luenberger productivity index. We focus on differences in the measures of productivity growth by distinguishing carbon emissions from either end-user industries or the electricity generation industry. Empirical results suggest three main findings. First, the efficiency of total emissions is higher than that of direct emissions except for the shipbuilding industry. Second, green productivity in the manufacturing sector increased during the study period. Finally, green productivity depends on the indirect emissions of each industry. These results indicate that policymakers need to deliberately develop policy tools for mitigating carbon emissions of the manufacturing industrial sectors based on our empirical findings.

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Research on green innovation countermeasures of supporting the circular economy to green finance under big data

Journal of Enterprise Information Management ◽

10.1108/jeim-01-2021-0039 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Zhao Yaoteng ◽

Li Xin

Keyword(s):

Big Data ◽

Environmental Regulation ◽

Fixed Time ◽

Spatial Effect ◽

Regression Coefficients ◽

Green Innovation ◽

Time And Space ◽

Content Type ◽

Green Finance ◽

Lag Model

PurposeThe purpose of this paper is to explore the sustainable development strategy of green finance under the background of big data.Design/methodology/approachFrom the perspective of big data, this paper uses quantitative and qualitative analysis methods to judge the correlation among green finance, environmental supervision and financial supervision. Green finance gives the entropy method to calculate the score of green finance and environmental regulation, and establishes the spatial lag model under the double fixed effects of time and space.FindingsSpatial autocorrelation test shows that economic spatial weight matrix has obvious spatial effect on green innovation. Through the model selection test, the space lag model with fixed time and space is selected. The regression coefficients of green finance, environmental regulation and their interaction are 0.1598, 0.0541 and 0.1763, respectively, which significantly promote green innovation. The regression coefficients of openness, higher education level and per capita GDP are 0.0361, 0.0819 and 0.0686, respectively, which can significantly promote green innovation.Originality/valueIn view of the current situation of large-scale application of big data technology in green innovation of domestic energy-saving and environmental protection enterprises, this paper establishes a fixed time lag evaluation model of green innovation. M-test statistics show that the original hypothesis with no obvious spatial effect is rejected.

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Research on Green Productivity of Chinese Real Estate Companies—Based on SBM-DEA and TOBIT Models

Sustainability ◽

10.3390/su12083122 ◽

2020 ◽

Vol 12 (8) ◽

pp. 3122

Author(s):

Zhao Yang ◽

Hong Fang

Keyword(s):

Real Estate ◽

Information Disclosure ◽

Negative Impact ◽

Malmquist Index ◽

High Energy ◽

Energy Utilization ◽

Productivity Index ◽

Utilization Rate ◽

Green Innovation ◽

Green Productivity

Apart from promoting social-economic development and increasing social employment, the real estate industry in China has also brought up problems such as high energy consumption and high emissions. Scholars now focus more on energy conservation, emission reduction and sustainable development of real estate companies in their current research. The data used by this paper are three-year panel data from 2015 to 2018, with observations from 15 representative real estate companies. CO2 and green credit index are introduced as the undesirable output and the green output of real estate companies respectively. First, with the DEA model and the Malmquist index model, this paper evaluates the green productivity of real estate companies statically and dynamically. The Tobit model is then employed by the author to analyze factors that may affect green productivity. Our results indicate that (1) the green productivities of 15 Chinese real estate companies have improved by various degrees. The average green productivity rises from 0.701 in 2015 to 0.849 in 2018, indicating that the energy utilization rate of enterprises has gradually increased. From the calculation and decomposition of the Malmquist total factor productivity index, we know that technological progress is vital in improving the green productivity of real estate companies. (2) As for the influencing factors, the green productivity is positively related to factors such as policy compliance indicator P, environmental responsibility commitment indicator R, indicator of green innovation capability I, and indicator of green development information disclosure M. The asset-liability ratio on the contrary has a negative impact on green productivity. It’s worth to point out that the green innovation index and green productivity is significantly correlated and the correlation coefficient can be up to 0.636, which implies that the key to improving green productivity is to increase research and development investment.

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