Provincial green economic efficiency of China: A non-separable input–output SBM approach

Xueping, Tao; Wang, Ping; Zhu, Bangzhu

doi:10.1016/j.apenergy.2016.02.133

Cited by 120 publications

(39 citation statements)

References 20 publications

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“…with: X-n × 1 vector of gross output (sales matrix) Y-n × 1 vector of final demand A-n × n matrix of input coefficients aij with n sectors in the economy (coefficient matrix) I-identity matrix The Input-Output methodology is commonly known in macroeconomic theory, and has been extensively applied to various problems and disciplines [27][28][29][30][31][32]. The main components of the methodology and data base are transactions tables, technical coefficients, and multipliers.…”

Section: Methodology and Datamentioning

confidence: 99%

Socio-Economic Implications of Drought in the Agricultural Sector and the State Economy

Ziolkowska

2016

Economies

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Abstract:In 2011, the most severe drought in Texas history caused $7.62 billion in losses in the agricultural sector alone. This paper analyzes ripple effects of the 2011 drought in Texas agriculture on the entire state economy retrospectively in an effort to foster discussion on targeted mitigation measures in the long term. By using an Input-Output and social accounting matrix model, direct effects on livestock, cotton, sorghum, wheat, corn, hay, and timber production, as well as indirect effects on other related sectors, and finally induced effects from changing consumers behavior have been estimated. According to the results, the 2011 drought caused economic losses of $16.9 billion in the entire Texas economy and increased the unemployment by around 166,895 people. The agricultural sector alone lost around 106,000 jobs. The cotton farming experienced 91% of revenue losses (as compared to 2010), while the livestock production lost 32% in revenue. The decreased production yields and limited market supply directly influence market prices for those products, which might create additional spillover effects on export and import quantities. The presented analysis can be helpful for designing policies to launch mitigation programs for drought events in the future.

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Section: Methodology and Datamentioning

confidence: 99%

Socio-Economic Implications of Drought in the Agricultural Sector and the State Economy

Ziolkowska

2016

Economies

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“…factors. Embodied in, based on the DEA model and comprehensive evaluation index system to measure green development efficiency [15][16][17][18][19][20][21][22][23]. And then using the Malmquist index decomposition, thayer index and exploratory spatial data analysis and other methods to analyze its space-time evolution law.…”

Section: Author Copy • Author Copy • Author Copy • Author Copy • Authmentioning

confidence: 99%

Spatial-Temporal Pattern and Influencing Factors of the Urban Green Development Efficiency in Jing-Jin-Ji Region of China

Cui¹,

Liu²

2021

Pol. J. Environ. Stud.

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As global problems such as the lack of resources and environmental degradation continue to intensify, the international community is increasingly aware of the importance of green development. At the same time, as a crucial guideline for China's ecological civilization construction, "Beautiful China" development, and global economic transition and restructure, green development will surely become the important engine for the green transformation and development of the Jing-Jin-Ji region and the whole country. Based on a thorough review of the concept of green development, this paper estimates the green development efficiency of 13 cities in Jing-Jin-Ji region of China in 2003-2017 by using the super efficiency SBM model. Then, by employing the coefficient of variation and the Global Moran's I, this paper further explores the spatio-temporal differentiation characteristics of the green development efficiency. Finally, the spatial panel model is used to explore the influencing factors of the green development efficiency. The results show the following: (1) The overall green development efficiency in Jing-Jin-Ji region of China declined steadily by 2% from 0.965 in 2003 to 0.948 in 2017. (2) The green development efficiency basically presents the spatial differentiation characteristics of the higher in the northern cities and the lower in the southern cities. (3) The green development efficiency has significant regional differences, and increased by 22% from 0.240 in 2003 to 0.295 in 2017. (4) Economic development level influence on the green development efficiency presents a "U" shaped curve that suppresses and then promotes. Industrial structure and the degree of openness have a positive influence, and urbanization have a negative influence on green development efficiency. Whereas, technological innovation has non-significant influence. Based on these findings, this paper proposes some targeted policy suggestions for promoting the green development efficiency.

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“…The GDP per capita of each province was used to measure the development of the economy. (2) The industrial structure (IND). Secondary industry is the industrial sector that processes raw materials.…”

Section: Control Variablesmentioning

confidence: 99%

“…Data envelopment analysis (DEA) as a linear programming method is widely applied for assessing the efficiency and productivity of decision-making units (DMUs). Tao et al [2] used the DEA-SBM (slacksbased measure) model to measure the efficiency of the provincial green economy in China, and Zhao et al [3] used the super-efficiency SBM model with undesired outputs to measure the efficiency of the Chinese green economy and analyzed its influencing factors. Wen et al [4] examined the differences in regional efficiency of China's construction industry by combining multiregional input-output and DEA methods.…”

Section: Introductionmentioning

confidence: 99%

The Relationship between Environmental Regulations and Green Economic Efficiency: A Study Based on the Provinces in China

Luo

Wang

et al. 2021

IJERPH

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This study examined the relationship between environmental regulations (ER) and green economic efficiency (GEE) based on the panel data of 30 provinces in China from 2008 to 2017. Firstly, GEE was calculated and evaluated using the super-efficiency SBM model with undesirable outputs. Secondly, the impact of ER on GEE was studied with the Tobit model. Finally, this article draws conclusions based on the above analysis and offers some suggestions for government and enterprise. The results show that the GEE of China is generally low. The GEE of the eastern region is much higher than that of the middle and western regions, with the western region performing slightly better than the middle. From west to east, there is a V shape, with high efficiency in the west and east and low efficiency in the middle. The impact of ER on GEE has the characteristics of nonlinearity and spatial heterogeneity. At the national level, as well as in the middle and western regions, the impact of ER on GEE shows an inverted U shape that first rises and then falls. ER are currently within the range conducive to the development of GEE. If the intensity of ER exceeds the critical value, they will have a negative impact on GEE. In the eastern region, the impact of ER on GEE is shown as a U shape that first falls and then rises. At present, the ER are not of sufficient intensity to contribute to the improvement of GEE. Only when the intensity of the ER exceeds the critical value will they have a positive influence on the GEE.

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Provincial green economic efficiency of China: A non-separable input–output SBM approach

Cited by 120 publications

References 20 publications

Socio-Economic Implications of Drought in the Agricultural Sector and the State Economy

Socio-Economic Implications of Drought in the Agricultural Sector and the State Economy

Spatial-Temporal Pattern and Influencing Factors of the Urban Green Development Efficiency in Jing-Jin-Ji Region of China

The Relationship between Environmental Regulations and Green Economic Efficiency: A Study Based on the Provinces in China

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