Establishing a Framework to Evaluate the Effect of Energy Countermeasures Tackling Climate Change and Air Pollution: The Example of China

Influence of emotion on purchase intention of electric vehicles: a comparative study of consumers with different income levels

Promoting electric vehicles (EVs) adoption has become one of the important paths for countries around the world to address climate change and accelerate the transformation of energy system for achieving sustainable development. As one of the important psychological factors, the research on the explanatory power of emotions to EVs purchase intention is still insufficient. This paper collected 400 valid questionnaires all around China. By incorporating emotions and moral norms into the Theory of Planned Behavior (TPB) model, this study used structural equation model to estimate the impact of positive anticipated emotion (PAE), negative anticipated emotion (NAE), and moral norms together with TPB elements on EVs purchase intention. In order to explore the heterogeneity effect of the above factors on EVs purchase intention among consumers of different income groups, we divided the total sample into high-income subsample and low-income subsample according to the household monthly disposable income. We concluded as follows: for the total sample, PAE has the greatest impact on EVs purchase intention, followed by attitude, NAE, and perceived behavioral control (PBC). In particular, the purchase intention of high-income consumers mainly depends on NAE, while the purchase intention of low-income consumers mainly depends on PAE. Additionally, PBC has more significant impact on EVs purchase intention of high-income group. Finally, targeted policy implications are proposed to promote EVs purchase.

Collaborative Optimization of Emissions and Abatement Costs for Air Pollutants and Greenhouse Gases from the Perspective of Energy Structure: An Empirical Analysis in Tianjin

Both air pollution and greenhouse effect have become important issues with regard to environmental protection both in China and across the world. Consumption of energy derived from coal, oil, and natural gas forms the main source of China’s major air pollutants, SO2 and NOX, as well as the major greenhouse gas CO2. The energy structure adjustment approach provides a sensible way, not only to achieve climate change mitigation and air pollutant reduction, but also to reduce abatement costs. In this paper, a multi-objective optimization method was adopted in order to analyze the collaborative optimization of emissions and abatement costs for both air pollutants and greenhouse gases. As a typical industrial city and economic center with fossil fuels as its main energy source, Tianjin of China is used as the research sample to prove that this method can mitigate air pollutants and greenhouse gas emissions and reduce abatement costs. Through demonstration, the results show that the optimization method proposed can reduce SO2, NOX, and CO2 emissions by 27,000 tons, 33,000 tons, and 29,000 tons, respectively, and the abatement costs will be reduced by 620 million yuan by adjusting the energy structure of Tianjin. The proposed method also suggests that China can achieve reductions of abatement cost and greenhouse gas and air pollutant emissions under the proposed energy structure. The results indicate that collaborative optimization would help China and other countries cope with climate change while improving domestic air quality.

Application of Fuzzy Optimization Model Based on Entropy Weight Method in Atmospheric Quality Evaluation: A Case Study of Zhejiang Province, China

A fuzzy optimization model based on the entropy weight method for investigating air pollution problems in various cities of Zhejiang Province, China has been proposed in this paper. Meanwhile, the air quality comprehensive evaluation system has been constructed based on the six major pollutants (SO2, NO2, CO, PM10, PM2.5 and O3) involved in China’s current air quality national standards. After analyzing the monthly data of six pollutants in 11 cities of Zhejiang Province from January 2015 to April 2018 by the above method, the authors found that, although the air quality of cities in Zhejiang Province did not reach the long-term serious pollution of Beijing, Tianjin, and Hebei, the air quality changes in the northern cities of Zhejiang were worse than those in southern Zhejiang. For example, the air quality of Shaoxing in northern Zhejiang has dropped by 14.85% in the last study period when compared with that of the beginning period, and Hangzhou, the provincial capital of Zhejiang, has also seen a decrease of 6.69% in air quality. The air quality of Lishui, Zhoushan and Wenzhou in southern Zhejiang has improved by 8.04%, 4.67% and 4.22% respectively. Apart from the geographical influence, the industrial structure of these cities is also an important cause for worse air quality. From the local areas in southern Zhejiang, cities have developed targeted air pollution control measures according to their own characteristics, including adjusting the industrial structure, changing the current energy consumption structure that heavily relies on coal, and improving laws and regulations on air pollution control, etc. In the four cities in central Zhejiang, the air quality at the end of the period (April 2018) has decreased from the beginning of the period (January 2015), given that there were no fundamental changes in their industrial structure and energy pattern.