Improving energy efficiency considering reduction of CO2 emission of turnip production:A novel data envelopment analysis model with undesirable output approach

Modern Turnip production methods need significant amount of direct and indirect energy. The optimum use of agricultural input resources results in the increase of efficiency and the decrease of the carbon footprint of turnip production. Data Envelopment Analysis (DEA) approach is a well-known technique utilized to evaluate the efficiency for peer units compared with the best practice frontier, widely used by researches to analyze the performance of agricultural sector. In this regard, a new non-radial DEA-based efficiency model is designed to investigate the efficiency of turnip farms. For this purpose, five inputs and two outputs are considered. The outputs consist turnip yield as a desirable output and greenhouse gas emission as an undesirable output. The new model projects each DMU on the strong efficient frontier. Several important properties are stated and proved which show the capabilities of our proposed model. The new models are applied in evaluating 30 turnip farms in Fars, Iran. This case study demonstrates the efficiency of our proposed models. The target inputs and outputs for these farms are also calculated and the benchmark farm for each DMU is determined. Finally, the reduction of CO2 emission for each turnip farm is evaluated. Compared with other factors like human labor, diesel fuel, seed and fertilizers, one of the most important findings is that machinery has the highest contribution to the total target energy saving. Besides, the average target emission of turnip production in the region is 7% less than the current emission

Regional allocation of carbon dioxide abatement in China

https://doi.org/10.1016/j.chieco.2011.06.00

Industry-level Total-factor Energy Efficiency in Developed Countries

This study computes and analyzes the total-factor energy efficiency (TFEE) of 11 industries in 14 developed countries during the period of 1995-2005 using the data envelopment analysis (DEA) approach. There are four inputs: labor, capital stock, intermediate inputs other than energy, and energy. The value added is the only output. The most inefficient industry is the metal industry, which has an average TFEE of 40.6%. Australia is the most inefficient country, with the lowest weighted TFEE in every year except for 1996 and 1998. The most efficient countries are the United States from 1995 to 1998, Denmark from 1999 to 2002, and Netherlands from 2003 to 2005. Given that the number of efficient industries decreases over time, it is clear that most industries have room to improve their energy efficiency as time goes by. Moreover, based on the total-factor framework, this study finds no support for the convergence of energy efficiency levels.Data envelopment analysis (DEA); Total-factor energy efficiency; Industry-level analysis

CO₂ emission reduction potential in China from combined effects of structural adjustment of economy and efficiency improvement

China has committed to decreasing its emission intensity by 60% to 65% by 2030 compared to 2005 levels and achieving carbon neutrality by 2060. It is of great importance to evaluate the CO2 emission reduction potential to quantify the amount of CO2 emissions that can be less generated and the amount that should be balanced out. Economic structure adjustment and CO2 emission efficiency improvement will contribute to mitigating CO2 emissions, which always happen simultaneously in the real world. However, few studies consider these issues simultaneously, which can lead to inaccurate estimation. A scenario analysis framework is proposed to estimate their combined effects, and an indicator is proposed to measure the technical feasibility of achieving the reduction potential. A set of scenarios are designed based on this framework and we find that: (1) to achieve carbon neutrality, 6161.16 Mt of CO2 emissions of China can be less generated compared to 2017 levels by significantly increasing its tertiary industry share to high-income entities’ level and adopting the most advanced technology to improve emission efficiency; the remaining 2732.40 Mt of CO2 emissions should be removed by carbon offsetting. Regarding emission intensity, 81.39% can be reduced compared with the 2005 level; and (2) Technical feasibility analysis shows Sichuan, Chongqing, and Anhui have the largest technical barriers in achieving the reduction potential. The proposed scenario analysis framework can provide a reference not only for China to achieve the emission mitigation pledges, but for countries with significant technological differences and structure adjustment to formulate mitigation strategies.</p

Carbon emissions intensity reduction target for China's power industry: An efficiency and productivity perspective

© 2018 Elsevier Ltd This paper proposes a scenario analysis to address whether the national and provincial CO2 emissions intensity reduction target during 2016–2020 would be achievable for China's power industry with the identification of change on carbon productivity. This productivity indicator is further decomposed to investigate contributions of different sources to productivity growth when there exists technological heterogeneity. Evaluation results show that even if all electricity-generating units in each region were able to adopt the best practice, the nationwide 18% intensity reduction target is not feasible through improving technical efficiency or upgrading technology on electricity generation and carbon abatement in a short or medium term. The existence of regional technological heterogeneity in power generation and associated CO2 emissions reduction processes implies the necessity of more differentiated regulations and policies for emission reduction across China's regions and inter-regional technology transfer. The emerging national emission trading scheme could easy some challenges in formulating emission policy for heterogeneous regions

Resource abundance, industrial structure, and regional carbon emissions efficiency in China

© 2019 Elsevier Ltd With increasing concerns over climate change and the global consensus regarding low carbon growth, the transition of resource-based regions has become urgent and challenging. We employ a Slacks-Based Measure with windows analysis approach to estimate the carbon emissions efficiency and abatement potential of China's provinces over the period of 2003–2016. A panel Tobit model is further employed to analyze the direct and indirect effects of natural resource abundance on emissions efficiency. We find that: (1) There exists a negative correlation between resource abundance and carbon emissions efficiency. The more abundant the resources, the lower the emissions efficiency. (2) Although emissions efficiency and abatement potential are generally negatively correlated, abatement potential also depends on the scale of the economy. (3) Resource dependence is unfavourable for the rationalization and advancement of the industrial structure, which indirectly affects the carbon emissions efficiency. These findings imply that resource-based regions should make the improvement of emissions efficiency and the exploration of abatement potential as their top priority of actions for a low-carbon transition, and promote the transformation of industrial structure in order to obtain a double dividend in sustainable development and carbon emissions efficiency

Analysis of Regional Differences and Influencing Factors on China’s Carbon Emission Efficiency in 2005–2015

With the challenge to reach targets of carbon emission reduction at the regional level, it is necessary to analyze the regional differences and influencing factors on China’s carbon emission efficiency. Based on statistics from 2005 to 2015, carbon emission efficiency and the differences in 30 provinces of China were rated by the Modified Undesirable Epsilon-based measure (EBM) Data Envelopment Analysis (DEA) Model. Additionally, we further analyzed the influencing factors of carbon emission efficiency’s differences in the Tobit model. We found that the overall carbon emission efficiency was relatively low in China. The level of carbon emission efficiency is the highest in the East region, followed by the Central and West regions. As for the influencing factors, industrial structure, external development, and science and technology level had a significant positive relationship with carbon emission efficiency, whereas government intervention and energy intensity demonstrated a negative correlation with carbon emission efficiency. The contributions of this paper include two aspects. First, we used the Modified Undesirable EBM DEA Model, which is more accurate than traditional methods. Secondly, based on the data’s unit root testing and cointegration, the paper verified the influencing factors of carbon emission efficiency by the Tobit model, which avoids the spurious regression. Based on the results, we also provide several policy implications for policymakers to improve carbon emission efficiency in different regions

Efficient distribution of carbon emissions reduction targets at the city level: A case of Yangtze River Delta region

The Chinese central government has released detailed carbon emissions abatement targets at the provincial level, but provides no specific emissions reduction targets at the city level. Most provincial governments simply allocate carbon emissions reduction tasks to their cities based on the GDP of their cities. Allocation approaches, however, should emphasize the most effective distribution to reach overall targets that reflect actual reduction capacities of cities. This paper proposes an allocation method at the city level by combining a data envelop analysis method, an entropy weight method and a clustering analysis method using the Yangtze River Delta region as a case study. Results of our analysis indicate that cities with higher carbon emissions abatement potentials, financial abilities, a larger number of above-scaled industrial enterprises and higher GDP are better positioned to reduce carbon emissions and should be assigned proportionately higher reduction targets. The merits and policy implications of the proposed approach are discussed in comparison to simply using GDP to allocate emission reduction targets