Applying data envelopment analysis approach to improve energy efficiency and reduce greenhouse gas emission of rice production

This study used a non-parametric method in determining the efficiency of farmers, discriminate efficient farmers from inefficient ones, identify wasteful uses of energy in order to optimize the energy inputs for broiler production, investigate the effect of energy optimization on greenhouse gas (GHG) emission and the actual total amount of GHG emission compared to the optimum quantity. A total sample size of 55 broiler farmers were selected from Kaduna State of Nigeria through a multi-stage sampling technique. Total energy used in various operations during broiler production was 77916.14 MJ (500 birds)-1. Results revealed that 63% of producers were technically efficient, while 43 producers under pure technical efficiency (PTE) were identified as efficient (79.6%). The mean values of technical efficiency (TE), PTE and scale efficiency (SE) of farmers were observed to be 0.976; 0.993 and 0.983, respectively. Further, 1.38% [1071.54 MJ (500 birds)-1] of overall input energies can be saved if the performance of inefficient farms rose to a high level. The study concludes that the total GHG emission can be reduced to the value of 981.08 Kg CO2eq by energy optimization.

Comparison of energy consumption and GHG emissions of open field and greenhouse strawberry production

Application of data envelopment analysis approach for optimization of energy use and reduction of greenhouse gas emission in peanut production of Iran

Optimization of energy required and greenhouse gas emissions analysis for orange producers using data envelopment analysis approach

Data for this research were elicited from 99 sesame farmers in Jigawa State, Nigeria via multi-stage sampling technique. Energy efficiency was studied and degrees of technical efficiency (TE), pure technical efficiency (PTE) and scale efficiency (SE) were determined using data envelopment analysis (DEA). Additionally, wasteful uses of energy by inefficient farms were assessed and energy saving of different sources was computed. Furthermore, the effect of energy optimization on greenhouse gas (GHG) emission was investigated and the total amount of actual and optimum GHG emission was compared. Results revealed that only 9.4 percent DMUs were technically efficient and the average TE score was 0.624; based on BCC model 34.4 percent DMUs were identified to be efficient and the mean PTE score was 0.79; while based on scale efficiency only 12.5 percent DMUs were efficient, and the mean SE score was 0.804. Furthermore it was observed that approximately 38.17 percent (1505.58MJhac') of overall input energies can be saved if performance of inefficient DMUs rose to a high level. Moreover, findings inferred that, by energy optimization, total GHG emission can be reduced to an estimated value of 21.87 KgCo2eq

Applying data envelopment analysis approach to improve energy efficiency and reduce GHG (greenhouse gas) emission of wheat production

Data for this study were obtained from province of Esfahan in Iran. 260 potato producers were randomly selected for data collection. Energy efficiency of potato growers was studied and degrees of technical efficiency (TE), pure technical efficiency (PTE) and scale efficiency (SE) were determined using data envelopment analysis (DEA). Additionally, wasteful uses of energy by inefficient farms were assessed and energy saving of different sources was computed. Furthermore, the effect of energy optimization on greenhouse gas (GHG) emission was investigated and the total amount of GHG emission of efficient farms was compared with inefficient ones. It was revealed that 21% of producers were technically efficient and the average of TE was calculated as 0.83. Based on the BCC model 105 growers were identified efficient (40%) and the mean PTE of these farmers was found to be 0.98. Also, it was concluded that 13% (11506.63 MJ ha -1 ) of overall input energies can be saved if the performance of inefficient farms rose to a high level. Finally it was concluded that, by energy optimization the total GHG emission can be reduced to the value of 2075.21 kg CO2eq.

The agricultural sector is a consumer as well as a producer of energy. This study explores the relationship between energy consumption and greenhouse gas emission of cotton crop in Punjab province of Pakistan. Standard energy equivalents megajoules (MJ) were used to measure energy from different inputs and output, similarly, a standard unit kilogram of carbon dioxide equivalent (kg CO2) was used to estimate direct and indirect greenhouse gas emissions from the use of farm inputs. A non-parametric data envelopment analysis was used to estimate the energy efficiency of cotton growers. Farm efficiency analysis tool (FEAT) was used to estimate greenhouse gas emission and its intensity. The results showed that a total of 58, 374.07 MJ ha−1 input energy was used in cotton production, and output energy was calculated as 33,134 MJ ha−1. The results of DEA showed that 33% and 61% farmers were technically and pure technically efficient, respectively. The technical, pure technical and scale efficiency score of the cotton growers were 0.77, 0.90 and 0.85, respectively. Optimum energy requirement was found to be 34,882.65 MJ ha−1 demonstrating that if recommendations are followed 22.65% of input energy can be saved. The total greenhouse gas (GHG) emission was calculated to be 1928 kg CO2 ha−1. GHG intensity a ratio of kg CO2eq emission per MJ of output energy produced was estimated to be 0.07 kg CO2/MJ in cotton production. As part of recommendations, energy management in term of efficient, sustainable and economic use of energy in cotton is highly recommended. Keywords: Energy use in agriculture, Input-output energy, GHG emission, Cotton, Pakistan

Major US electric utility climate pledges have the potential to collectively reduce power sector emissions by one-third

Faced with two big stresses of energy shortage and environmental pollutants, China should improve its energy utilization efficiency. Based on the data of China Statistical Yearbook and China Environmental Statistics Yearbook, the pollutants discharge and energy utilization efficiency, including technical efficiency (TE), pure technical efficiency (PTE), scale efficiency (SE) and returns to scale (RTS) of China’s industry and its sub-sectors were analyzed by constant returns to scale model (CRS) and variable returns to scale model (VRS) of non-parametric data envelopment analysis (DEA) method. Results showed that: (1) The RTS of China's total industrial environmental efficiency and energy utilization efficiency were all in "irs" state, indicating that it was beneficial to expand the entire industrial scale. (2) The TE of total industrial energy utilization efficiency was about 0.80, the minimum TE was 0.018 of production and distribution of gas sector. (3) The total industrial environmental efficiency was about 0.77, the two sectors with high pollutants discharges were mining of other ores and manufacture of paper and paper products, and TE were 0.065 and 0.067, respectively. Mostly industrial sub-sectors should improve their technologies and adjust its scales except for extraction of petroleum and natural gas, manufacture of tobacco, printing, reproduction of recording media and so on. (4) Mining of other ores, manufacture of tobacco, manufacture of communication equipment, computers and other electronic equipment, manufacture of measuring, instruments and machinery for cultural activity and office work and production and distribution of water were in high energy utilization efficiency while in low environmental efficiency and steady RTS. So these sectors should improve the technologies to achieve DEA effective. (5) Scale expanding, technology advancement, energy use pattern improvement and industry structure adjustment were suggested for energy-saving industry according to the TE, PTE, SE and RTS.

Greenhouse gas (GHG) emissions reduction in the electricity sector: Implications of increasing renewable energy penetration in Ghana's electricity generation mix

Optimization of energy required for alfalfa production using data envelopment analysis approach

Optimization of energy consumption of dairy farms using data envelopment analysis – A case study: Qazvin city of Iran

This paper investigates the impact of several comprehensive risks such as credit risk, capital risk, liquidity risk, and insolvency risks on Pakistani banks' technical efficiency to assess the nexus between environmental investments with technical efficiency of banks. It also probes into the effect of competition among the Pakistani banks on technical efficiency. The data envelopment analysis (DEA) CCR and BCC models are used to estimate technical, purely technical, and scale efficiencies of the Pakistani banks. The Lerner index measures the banking competition. For estimation, the bootstrap truncated regression is used as an econometric technique. The robustness of results is cross-checked by using an alternative econometric technique (fractional logit regression) and an alternative competition measure (Boone indicator). The study revealed that capital risk has a positive impact on scale efficiency and insolvency risk has a negative impact on technical and pure technical efficiencies. Similarly, there is a positive significant relationship between technical efficiency and environmental investment. Furthermore, the competition has a significant negative effect on Pakistani banks' technical and pure technical efficiencies. The results suggest that the efficiency of the Pakistani banks is significantly affected by bank size, taxation, diversification, operational cost management, banking development, trade openness, and infrastructure development, which ultimately promotes environmental efficiency and protection. The comparative study indicates that the state-owned banks have higher technical, pure technical, and scale efficiencies than the private, foreign, and Islamic banks.

Harmonizing the quantification of CCS GHG emission reductions through oil and natural gas industry project guidelines