Beta decoupling relationship between CO2 emissions by GDP, energy consumption, electricity production, value-added industries, and population in China

Abstract

The credible sources of fossil energy efficiently are a vital cause of economic growth and considerable influence on adequate security. Whereas radiant energy positively enhances or ostensibly promotes socio-economic stability and the controlled environment. The fossil energy sources supply has become progressively stern in China and reconnoitering the beta decoupling relationships between CO2 emissions, GDP, energy consumption, electricity consumption, value-added industries, and population. The results will be favorable for illustrative the security of the valuable resources. This study adopts the extended stochastic model (STIRPAT) with Beta Decoupling Techniques (BDT). This modern technique merely employs the decoupling situation by the alpha and beta effects from 1989 to 2018 and calculates the % change in CO2 emissions by GDP growth and energy consumption. The estimated results represent negative and economic growth depends on coal and natural gas. First, CO2 emissions annually increasing cause of rapid growth, energy consumption, and electricity production, and the structural contradiction of energy remained static. Second, the Value-added industries estimated that CO2 emissions reduce by primary industries. Third, the decoupling states of CO2 emissions and population show an inverse relationship. This paper tentatively suggests China is sustainable, naturally strengthens energy output, transmutes the energy consumption structure, and advances development policies under environmental circumstances.

Impacts of Energy Efficiency and Economic Growth on Air Pollutant Emissions: Evidence from Angara–Yenisey Siberia

“…Although most recent STIRPAT applications use the panel data to explore both the spatial and temporal heterogeneity of the pressure of social and economic development on the environment (e.g., [15,28,[56][57][58]), the original works by York, Dietz, and Rosa [27,31] were made using static cross-sectional samples. We use this approach and Formula (3) as a general form of regression due to the lack of available data for some critical variables.…”

“…Most cities were affected by the emissions of specific pollutants: particulate matter (209), nitrogen dioxide (231), nitrogen oxide (136), sulfur dioxide (225), carbon monoxide (208), benzapyrene (174), and formaldehyde (150). The highest level of air pollution was reported for 18 Russian cities geographically located in industrially developed areas in Siberia and the Russian Far East, specifically in the Angara-Yenisey rivers basin (15).…”

“…In recent years, the literature on establishing diverse interlinkages between air pollution, economic growth, and social development of territories became more focused on regional-and local-scale studies rather than cross-country comparisons. The growing interest in ecological topics drives the development of data collection and methods for testing various hypotheses, such as the negative impact of air pollution on housing prices [10,11], effects of urban planning quality on the local environmental conditions [12][13][14], the presence of decoupling between energy consumption and economic growth [15][16][17], and so forth.…”

“…Dietz and Rosa suggested a stochastic version of IPAT, then called STIRPAT (Stochastic Impacts by Regression on Population, Influence and Technology) [27]. STIRPAT allows the empirical application of the IPAT concept to test hypotheses about the contribution of different structures of growth factors to environmental pressures [15,[28][29][30][31][32]. In past years, evidence also emerged that merging both IPAT and EKC concepts is beneficial to better understand the genuine effects of economic and social development for the state of the environment [33,34].…”

Performance evaluation of e-commerce firms in China: Using three-stage data envelopment analysis and the Malmquist productivity index

“…The traditional three-stage DEA mode is constructed as follows: DEA-stochastic frontier analysis (SFA)-DEA [14][15][16]. In the first stage, an input-oriented BCC or CCR is applied to examine the efficiency of DMUs and input slack [17,18].…”