A comprehensive eco-efficiency model and dynamics of regional eco-efficiency in China

Abstract:The transport sector is the major green-house gas emitter and most rapidly growing sector in terms of consuming energy in China. Understanding the driving forces behind carbon emission is a prerequisite for reducing carbon emissions and finding a balance between economic growth and carbon emissions. The purpose of this paper is to identify the impact of the factors which influence the level of carbon emissions from the transportation sector in the Beijing-Tianjin-Hebei (BTH) area, China, using decomposition model, combined with a decoupling elasticity index. The results of our study indicate that: (1) changes in the level of carbon emissions from the transportation sector are not always synchronized with changes in economic growth. (2) The decoupling state between the carbon emissions and economic growth of Tianjin and Beijing can be roughly divided into two phases. The first phase was during the 2005 to 2009 period, when the decoupling state was pessimistic. The second phase was from 2009 to 2013, when the decoupling state became better overall and was mainly dominated by weak decoupling. Conversely, the decoupling state of Hebei was mainly weak during this period. (3) Economic growth and population size play positive roles in increasing the levels of transportation-related carbon emissions in BTH. However, the energy structure is a negative force. The effect of energy intensity always plays a negative role in Tianjin and Hebei, but positive in Beijing. The industrial structure effect shows a fluctuating trend, but the cumulative effect value is negative, and negative interaction is prominent. Finally, this paper gives some suggestions on how to develop low-carbon transport in BTH area.

Section: Literature Reviewmentioning

confidence: 99%

An Analysis of Decoupling and Influencing Factors of Carbon Emissions from the Transportation Sector in the Beijing-Tianjin-Hebei Area, China

Zhu

2017

“…In 2015, the energy consumption reduction rate per 10,000 RMB gross regional product in the Beijing, Tianjin, and Hebei provinces was 6.17% (Beijing), 7.21% (Tianjin), and 6.14% (Hebei). The differences between the three industries' reduction rates of 10,000-yuan industrial added value of energy consumption were even more striking, with rates of 8.16%, 13.25%, and 6.02%, respectively. Therefore, identifying the differences in energy efficiency between the three provinces and exchanging energy utilization experience will be conducive to lessening the industry energy efficiency differences in the Jing-Jin-Ji region.…”

Section: Introductionmentioning

confidence: 92%

Analysis of Total Factor Energy Efficiency and Its Influencing Factors on Key Energy-Intensive Industries in the Beijing-Tianjin-Hebei Region

Xiang

Jia

et al. 2018

Abstract:In order to realize the synergistic optimization management of energy efficiency in the key energy-intensive industries of the Beijing-Tianjin-Hebei (Jing-Jin-Ji) region, this paper calculates the total factor energy efficiency (TFEE) of 27 industries in the Jing-Jin-Ji region. We discover that the manufacturing of raw chemical materials and chemical products, the smelting and processing of ferrous metals, and the production and supply of electric power and heat power are key industries, considering their economic output ratio, energy consumption ratio, and energy efficiency. Then, the Malmquist index is used to decompose the TFEE of key energy-intensive industries. The results show that the TFEE changes in the three major industries in the Jing-Jin-Ji region are caused by technological progress. Hebei has the highest total factor average energy efficiency in the production and supply of electric power and heat power industry, the main reason for this being the spillover effect from Beijing enterprises that have led to significant technological changes in Hebei. Due to similar technological advancements, Tianjin has the highest total factor average energy efficiency in the manufacturing of raw chemical materials and chemical products and the smelting and processing of ferrous metals. Therefore, the Jing-Jin-Ji region should work to increase its technological innovation and enhance its core competitiveness. We should optimize the allocation of resources in specific industries to improve the scale efficiency.

“…Wang et al [77] constructed three levels of regional eco-efficiency indicators by analyzing the flow of materials and implemented DEA to measure the eco-efficiency degree of Tongling City between 1990 and 2008. Huang et al [78] proposed an extended DEA model that combines global benchmark technology, undesirable output, super efficiency and slacks-based measure to investigate the dynamics of regional eco-efficiency in China from 2000 to 2010. Üstün [79] used DEA to evaluate environmental efficiency of Turkish cities.…”

Section: The Academy-oriented Contributionmentioning

confidence: 99%

Ecological Efficiency Based Ranking of Cities: A Combined DEA Cross-Efficiency and Shannon’s Entropy Method

Storto

2016

Abstract:In this paper, a method is proposed to calculate a comprehensive index that calculates the ecological efficiency of a city by combining together the measurements provided by some Data Envelopment Analysis (DEA) cross-efficiency models using the Shannon's entropy index. The DEA models include non-discretionary uncontrollable inputs, desirable and undesirable outputs. The method is implemented to compute the ecological efficiency of a sample of 116 Italian provincial capital cities in 2011 as a case study. Results emerging from the case study show that the proposed index has a good discrimination power and performs better than the ranking provided by the Sole24Ore, which is generally used in Italy to conduct benchmarking studies. While the sustainability index proposed by the Sole24Ore utilizes a set of subjective weights to aggregate individual indicators, the adoption of the DEA based method limits the subjectivity to the selection of the models. The ecological efficiency measurements generated by the implementation of the method for the Italian cities indicate that they perform very differently, and generally largest cities in terms of population size achieve a higher efficiency score.Keywords: cross-efficiency; data envelopment analysis; Shannon's entropy; ecological efficiency; cities; ranking; Italy BackgroundIn the last decade, cities have gained a greater centrality in the economic and social growth of nations. The recent report delivered by the Brookings Institution indicates that in 2014 the economies of the 300 largest metropolitan areas accounted for 47% of global gross domestic product (GDP) and 38% of GDP growth [1]. According to recent estimates provided by Seto et al.[2], more than 80% of the national gross domestic product (GDP) is generated in urban areas. A recent worldwide research conducted by the McKinsey Global Institute on a sample of 600 cities estimates that between 2010 and 2050, the GDP of these cities is expected to double, while 23 megacities-cities having more than 10 million inhabitants-in 2007 generated 14% of global GDP [3].People move to and live in cities to have access to better jobs, education, health care, goods and services. More than half of the human population over the world is living in cities and towns, and, in the next decades, the number of people expected to live in cities will grow to 75%, while population growth over the next 25 years will be concentrated in cities and towns [4,5]. The most urbanized areas are located in the American and Europe continents, respectively having about 80% and 70% of all inhabitants residing in cities and towns. However, while cities are the primary source of economic development and social prosperity, and house more than half of the world population, they are large users of resources, responsible for about 2/3 of energy demand and greenhouse gas emissions [5]. The recent rapid and intense urbanization has often resulted in an over consumption of water, energy, raw