Decomposition analysis of the impact of economic growth on ammonia and nitrogen oxides emissions in the European Union

Hnatyshyn, Maria

doi:10.14254/2071-8330.2018/11-1/15

Cited by 35 publications

(31 citation statements)

References 5 publications

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“…However, the selected sample cannot be changed subjectively in a short period of time. Researchers have many inconsistent empirical results on the factors that influence the efficiency of green innovation [47][48][49][50][51][52], but they generally believe that environmental regulation, enterprise scale, technology introduction costs, and government support have significant effects on green innovation efficiency [53][54][55].…”

Section: Influencing Factorsmentioning

confidence: 99%

Evaluation Research of Green Innovation Efficiency in China’s Heavy Polluting Industries

2019

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Recently, green innovation efficiency, which considers innovation and environmental factors, is gradually becoming important for the sustainable development of Chinese heavy polluting industries because of the increasing strictness in China’s environmental regulations. Previous studies ignore the impact of external environmental factors on the efficiency of green industry innovation and fail to explain the complex relationship between environmental and technical efficiency fully. Therefore, a non-radial directional distance function-data envelopment analysis (DDF-DEA) three-stage green innovation efficiency evaluation model was constructed to measure the green innovation efficiency of China’s heavy polluting industries objectively and explore the impact mechanism of external factors. Then, the aforementioned model was used to conduct an empirical test on China’s heavy polluting industries. Results indicate that the green innovation efficiency of heavy polluting industries is generally low in China, and the entire industry is in the transitional stage of “effective innovation but not green.” The uncertainty of the effect of the environmental regulation policy, the over-reliance on external technologies, and the scale diseconomies of industries, which are the key factors in improving the green innovation efficiency of China’s heavy polluting industries, have a significant negative impact on green innovation efficiency. The conclusions of this study can provide a useful reference for China and other emerging markets to formulate reasonable environmental regulations and green transition of heavy polluting industries.

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Section: Influencing Factorsmentioning

confidence: 99%

Evaluation Research of Green Innovation Efficiency in China’s Heavy Polluting Industries

2019

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“…Addressing air pollution while advancing economic development tends to be a conundrum for developing countries [1,2]. Air pollution is also a disquieting phenomenon affecting the environment as well as presenting climate and human health problems to communities [3].…”

Section: Introductionmentioning

confidence: 99%

Atmospheric Pollution Mapping of the Yangtze River Basin: An AQI-Based Weighted Co-Word Analysis

Song

Liu

Chen

et al. 2020

IJERPH

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The purpose of this paper is to analyze the characteristics and human effects of atmospheric pollution in the Yangtze River Basin (YRB). An AQI(Air Quality Index)-based weighted co-word method is applied to explore the characteristics of keywords taken from the data, using authoritative media sources and government reports. Hierarchical clustering techniques are utilized to classify and visualize the keywords and display the different types of incidents. The results reveal the following four main clusters: enterprise pollution, coal-burning pollution, traffic pollution, and air pollutants. Cluster 1 is divided into 7 sub-clusters to offer powerful insight into the structural characteristics of industrial activities. This study is one of the first attempts to use a bibliometric approach to visualize the underlying and interconnected sub-clusters from grey data. It also provides an atmospheric pollution mapping for formulating government policies by understanding the human effects of air pollution incidents.

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“…In recent years, many scholars have been devoted to studying the energy markets and the relationships between energy and the economy [6][7][8][9][10][11]. For instance, Tvaronavičienė et al [6] employed long-range energy-forecasting software LEAP to predict the energy efficiency for selected European countries (Poland, Lithuania, and Germany).…”

Section: Introductionmentioning

confidence: 99%

“…Their findings show that the interrelationship between heat and electricity acquires a complementary nature, and the rise in prices for one commodity will not necessarily lead to a significant increase in demand for the other. Hnatyshyn [9] employed econometric model to investigate the environmental consequences of economic growth. The results indicate that the relationship between per capita income and emissions of both NH 3 and NOx falls into the Environmental Kuznets curve pattern.…”

Section: Introductionmentioning

confidence: 99%

Planning an Energy–Water–Environment Nexus System in Coal-Dependent Regions under Uncertainties

Chen

Zeng

et al. 2020

Energies

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Energy, water, and environment are inextricably interwoven in the complex social and economic networks. This study proposes an optimization model for planning the energy–water–environment nexus system (EWENS) through incorporating the linear autoregressive integrated moving average model prediction model (ARIMA), Monte Carlo simulation, chance-constrained programming (CCP), and type-2 fuzzy programming (T2FP) into one general framework. This method effectively tackles type-2 fuzzy set and stochastic uncertainties. The proposed model can quantitatively explore the interconnections between water, energy, and environment systems and generate an optimized solution for EWENS. The proposed model was applied to a coal-dominated region of China, i.e., Inner Mongolia. Several findings and policy implications were obtained. First, the total water supply for energy-generating activities will range from 1368.10 × 106 m3 to 1370.62 × 106 m3, at the end of planning periods. Second, the electricity for water supply will range from 2164.07 × 106 kWh to 2167.65 × 106 kWh at the end of the planning periods, with a growth rate of 46.06–48.72%. Thirdly, lifecycle carbon dioxide emission (LCDE) is projected to range from 931.85 × 106 tons to 947.00 × 106 tons at the end of the planning periods. Wastewater and SO2, NOx, and particulate matter (PM) emissions are projected to be 42.72 × 103–43.45 × 103 tons, 183.07 × 103–186.23 × 103 tons, 712.38 × 103–724.73 × 103 tons, and 38.14 × 103–38.80 × 103 tons at the end of the planning periods. Fourthly, as the largest electricity-exporting city of China, Inner Mongolia’s electricity outflows will export 1435.78 × 106 m3 of virtual water to other regions, implying that Inner Mongolia is pumping its important water resource to support other regions’ electricity demands. Finally, high carbon mitigation levels can effectively optimize the electricity power mix, reduce consumption amounts of water and coal, and mitigate air pollutants, wastewater, and LCDE. The obtained results provide useful information for managers to develop a sustainability plan for the EWENS.

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Decomposition analysis of the impact of economic growth on ammonia and nitrogen oxides emissions in the European Union

Cited by 35 publications

References 5 publications

Evaluation Research of Green Innovation Efficiency in China’s Heavy Polluting Industries

Evaluation Research of Green Innovation Efficiency in China’s Heavy Polluting Industries

Atmospheric Pollution Mapping of the Yangtze River Basin: An AQI-Based Weighted Co-Word Analysis

Planning an Energy–Water–Environment Nexus System in Coal-Dependent Regions under Uncertainties

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