Energy consumption, CO2 emissions, and agricultural disaster efficiency evaluation of China based on the two-stage dynamic DEA method

Study on the influence of industrial intelligence on carbon emission efficiency-empirical analysis of China's Yangtze River Economic Belt

How to achieve the goal of "carbon peak and carbon neutrality" and explore the compatibility of industrial and ecological civilization is a major challenge for China today. This study analyzes the impact of industrial intelligence on industrial carbon emissions efficiency in 11 provinces of China's Yangtze River Economic Belt, measuring the efficiency of industrial carbon emissions through the non-expected output slacks-based measure (SBM) model, selecting industrial robot penetration to measure the level of industrial intelligence, establishing a two-way fixed model to verify the impact of industrial intelligence on carbon emission efficiency, and testing for intermediary effects and regional heterogeneity. The results show that: (1) the industrial carbon emission efficiency of the 11 provinces shows year-over-year improvement, with significant differences between upstream, midstream, and downstream, where downstream is the highest and upstream is the lowest. (2) The development of industrial intelligence is highly uneven, with the upstream level being the weakest. (3) Industrial intelligence can improve the efficiency of industrial carbon emissions by enhancing green technological innovation and energy use efficiency. (4) The effect of industrial intelligence on industrial carbon emission efficiency also shows regional heterogeneity. Finally, we present policy recommendations. This research provides mathematical and scientific support for achieving carbon reduction targets at an early stage and helps accelerate the construction of a modern, low-carbon China.

Spatio-temporal efficiency of fiscal environmental expenditure in reducing CO2 emissions in China's cities

Although market-based CO₂ emission control measures (e.g., carbon tax and carbon trading market) have been deeply discussed, government-based measures have received limited attention. This has led to increased uncertainty regarding the formulation of targeted emission reduction policies. Using a unique dataset, the non-radial directional distance function, a proposed spatial meta-frontier analysis method, and the log t convergence model, this study comprehensively investigates the spatio-temporal trends in fiscal environmental expenditure efficiency (FE) and corresponding causes for in a case study for 106 Chinese cities over 2007-2019. The results show that city-level FE presented a slow upward trend at a relatively low level, with a clearly skewed distribution. The technology gap effect between city groups and the overall best production technology, and the efficiency gap effect within city groups were the main drivers widening the overall FE gap. Convergence analysis indicated that three convergence clubs of FE were found, which were distributed across the country. This study highlights that, when constructing fiscal environmental expenditure policies, the government should focus on balancing the regional gap of FE while comprehensively improving FE.

A systematic literature review of data envelopment analysis implementation in agriculture under the prism of sustainability

Safeguarding natural resources and energy is essential to ensure food security for future generations. Given the increase of published papers in the agricultural field applying Data Envelopment Analysis (DEA), this review seeks to address the special requirements of this methodology when implemented in the agricultural sector as well as to classify papers under sustainability aspects (economic, environmental, social). More specifically, 120 papers from Scopus and Web of Science databases were included in this review by using PRISMA methodology, and they were tested in the following groups (i) General information, (ii) DEA implementation, (iii) DEA extensions, (iv) Data type, (v) Data collection and processing, and (vi) Sustainability dimensions. Results indicate that there is a great need for weights use when performing DEA in the agricultural sector, to acquire results with greater explanatory power. Moreover, systematic data collection of multiple factors could lead to the implementation of complex methodologies, providing feasible solutions to the involved stakeholders. Lastly, the social aspect is the least represented dimension out of the three aspects of sustainability, indicating the need for the integration of social factors in such analyses, especially when DEA is used to create a policy framework in a specific area.

Decomposition and decoupling analysis of carbon emissions from agricultural economic growth in China's Yangtze River economic belt

In this study, carbon emissions from agricultural energy consumption (CEAEC) are fully analyzed using data from the Yangtze River Economic Belt (YEB) between 2000 and 2017. First, generalized LMDI is adopted to decompose the drivers of CEAEC into five components. Then, the decoupling indicator and the decoupling effort indicator are constructed to quantify the decoupling degrees and examine the government's emission reduction efforts, respectively. The results show that (1) CEAEC in the YEB has shown a phased increase, reaching a peak at 1732.2510⁴t in 2012. Except for some decreases found in Shanghai, Chongqing, and Guizhou, it is shown that all provinces' CEAEC have risen to varying degrees. In contrast, the intensity of CEAEC in the YEB has been declining since 2005. (2) The economic output effect acts as the major contributor to the growth of CEAEC, followed by the population effect. In contrast, both the energy intensity effect and the energy structure effect are the primary reasons for reductions in CEAEC. The spatial difference in CEAEC in the YEB increased significantly from 2000 to 2017. (3) There was an alternating change from decoupling to coupling and then to negative decoupling from 2000 to 2017. Based on the conclusions mentioned above, it is proposed that the formulation of low-carbon agricultural development strategies should consider the structural adjustment of agricultural energy consumption and the advancements of agricultural technology.

Sustainability efficiency of climate change and global disasters based on greenhouse gas emissions from the parallel production sectors - A modified dynamic parallel three-stage network DEA model

This study employed dynamic three-stage network data envelopment analysis (DEA), considering parallel production in the agricultural and industrial sectors, to assess the impact of greenhouse gas emissions on the climate change and natural disaster stages. The results revealed the following: (1) The dynamic overall efficiencies of more countries are decreasing than are increasing. The seven countries with the poorest overall efficiency ranking (Myanmar, Vietnam, Thailand, Bangladesh, the Philippines, Pakistan, and India) are mostly located in Southeast Asia. (2) The number of countries that maintained low efficiency over the long term is greater than those that retained high efficiency over the long term. Myanmar, Mexico, India, the Philippines, Thailand, and Vietnam maintained efficiency scores below 0.5, whereas South Korea, Japan, China, and New Zealand maintained efficiency scores above 0.8. (3) More than one-third of countries exhibited declines in efficiency over time, and half were European countries. Less than one-third of countries maintained their efficiency, and less than one-third of countries gradually improved. (4) Approximately half of the countries' efficiency scores were lower than the global average. The efficiency scores of the industrial sector exhibited a greater room for improvement on the input factors than did those of the agricultural sector. (5) Total factor energy efficiency analysis revealed that methane emissions and CO₂ emissions have a similar level but large room for improvement across countries. Improving input factors in the production stage can ultimately mitigate inefficiencies in the climate change and natural disaster stages. There are still other important factors related to climate change, such as sea surface temperature, forest areas, or air pollution indicators, that could be considered in future research. The occurrence of global disasters could also be discussed in groups according to the region where the countries are located in the future.

The cyclical impact of innovation in green and sustainable technologies on carbon dioxide emissions in OECD economies

This paper proposes a novel model for the cyclical and non-linear association between innovation in green and sustainable technologies and carbon dioxide (CO₂) emissions using foreign direct investment, gross domestic consumption, and renewable energy consumption as control variables for OECD economies. First, the findings validated the long-run cointegration among variables. Second, the significant long-term negative nexus between renewable energy consumption, positive shocks to innovation in green and sustainable technologies, and CO₂ emission was validated. Third, income per capita (GDP) and the negative shocks to innovation in green and sustainable technologies contributed to the CO₂ emissions. Based on these findings, this study offers some policy implications to mitigate CO₂ emissions.

Do international collaborations in environmental-related technology development in the U.S. pay off in combating carbon dioxide emissions? Role of domestic environmental innovation, renewable energy consumption, and trade openness

Many economies are seeking new ways to improve environmental quality through international collaboration in environmental-related technology development (ICERTD). Cost reduction, green market penetration, and green technology development are central to global partnerships for sustainable development, even though no empirical study explains the ICERTD-carbon dioxide (CO₂) emissions nexus. The paper fills this knowledge gap in the environmental economics literature by examining the relationship between ICERTD and CO₂ emissions in the U.S. from 1990Q1 to 2018Q4 using domestic environmental innovation, trade openness, renewable energy consumption, and gross domestic product per capita as control variables. Fully modified ordinary least squares, dynamic ordinary least squares, and correlated component regression methods were employed for testing the long-run nexus among the variables. The present study revealed that (i) a long-run cointegration existed among ICERTD, domestic environmental innovation, trade openness, renewable energy consumption, gross domestic product per capita, and CO₂ emissions; (ii) ICERTD, domestic environmental innovation, and renewable energy consumption benefited the U.S. in lowering CO₂ emissions in the long run; and (iii) trade openness and gross domestic product per capita were positively associated with CO₂ emissions. This study recommends important policy recommendations for increasing ICERTD for decarbonization.

Environmental efficiency of disaggregated energy R&D expenditures in OECD: a bootstrap DEA approach

Two essential topics of Sustainable Development Goals (SDGs) are accessible which are clean energy (SDG-7) and climate change action (SDG-13). Developments and innovations in energy technologies play an essential role in achieving these goals. Therefore, any country should use energy R&D expenditures, which are the primary source of energy innovation, most optimally. This paper aims to investigate the environmental efficiency of R&D expenditures for energy efficiency, renewable energy, hydro and fuel cells, fossil energy, nuclear energy, and other power and storage technologies in OECD countries using data envelopment analysis (DEA) and bootstrap DEA. Estimation findings indicate that only the USA ensures the environmental efficiency in energy R&D expenditures among OECD countries. Although Japan, Canada, France, Germany, and Italy cannot provide environmental efficiency in energy R&D, their scores are very close to the efficiency frontier. Portugal, Hungary, and Slovak Republic are the countries with the lowest environmental efficiency in energy R&D expenditures. At the end of the investigation, this paper also provides an empirical estimation of the extent to which inefficient countries should change their R&D spending to achieve efficiency.

Efficiency assessment of coal energy and non-coal energy under bound dynamic DDF DEA

The demand for energy has continued to increase because of global economic development, which has led to rising fuel prices and continued pollution problems. China is currently the largest coal consumer and is also the largest emitter of coal-fired CO₂ emissions. However, past efficiency studies have been mostly limited to static analyses and have not considered undesirable outputs. Therefore, this study developed a bound dynamic directional distance function (DDF) data envelopment analysis (DEA) model to explore the energy and environmental efficiencies in 30 Chinese provinces from 2011 to 2015, from which it was found that (1) the overall efficiency was the best in the eastern region, but relatively low in the western region; (2) Beijing, Guangdong, Jiangsu, Shandong, Shanghai, Tianjin, Jiangxi, Jilin, and some other regions had efficiencies of 1; (3) the revenue and non-coal indicator efficiencies were reasonably good, but the expenditure and emissions efficiencies were generally poor; and (4) the key direction for primary improvements was found to be the emissions index.