Agrarian Crisis and Steps to Combat It: Evidence of GHG Emission (CO2) in Sesame Production in Jigawa State, Nigeria

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

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

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

Determination of efficient and inefficient units for watermelon production-a case study: Guilan province of Iran

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

BackgroundSocietal pressures exist to reduce greenhouse gas (GHG) emissions from farm animals, especially in beef cattle. Both total GHG and GHG emissions per unit of product decrease as productivity increases. Limitations of previous studies on GHG emissions are that they generally describe feed intake inadequately, assess the consequences of selection on particular traits only, or examine consequences for only part of the production chain. Here, we examine GHG emissions for the whole production chain, with the estimated cost of carbon included as an extra cost on traits in the breeding objective of the production system.MethodsWe examined an example beef production system where economic merit was measured from weaning to slaughter. The estimated cost of the carbon dioxide equivalent (CO2-e) associated with feed intake change is included in the economic values calculated for the breeding objective traits and comes in addition to the cost of the feed associated with trait change. GHG emission effects on the production system are accumulated over the breeding objective traits, and the reduction in GHG emissions is evaluated, for different carbon prices, both for the individual animal and the production system.ResultsMultiple-trait selection in beef cattle can reduce total GHG and GHG emissions per unit of product while increasing economic performance if the cost of feed in the breeding objective is high. When carbon price was $10, $20, $30 and $40/ton CO2-e, selection decreased total GHG emissions by 1.1, 1.6, 2.1 and 2.6% per generation, respectively. When the cost of feed for the breeding objective was low, selection reduced total GHG emissions only if carbon price was high (~ $80/ton CO2-e). Ignoring the costs of GHG emissions when feed cost was low substantially increased emissions (e.g. 4.4% per generation or ~ 8.8% in 10 years).ConclusionsThe ability to reduce GHG emissions in beef cattle depends on the cost of feed in the breeding objective of the production system. Multiple-trait selection will reduce emissions, while improving economic performance, if the cost of feed in the breeding objective is high. If it is low, greater growth will be favoured, leading to an increase in GHG emissions that may be undesirable.

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.

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

Estimation of energy efficiency and greenhouse gas emission of cotton crop in South Punjab, Pakistan

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

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

Based on partial data, this paper uses BP neural network optimised by particle swarm optimisation algorithm to predict the total greenhouse gas (GHG) emissions of the line in the construction phase. The GHG emission efficiency is analysed by SBM (Slacks-Based Measure) super efficiency method. Finally, the grey relational analysis (GRA) is applied to sort the GHG emission correlation factors. Based on the existing design and quota document data of 16 stations and 16 sections of the Wuhu Monorail Line 1, we have employed a neural network optimized by particle swarm optimization algorithm to predict the total emissions of greenhouse gases during the construction phase of the entire line consisting of 25 stations and 24 sections. The GHG emissions of all stations and sections are 29,300 tons and 21,000 tons. The technical efficiency, pure technical efficiency, and scale efficiency of the stations and sections were high. As for stations, the order of influence degree is metal material consumption (0.9731) > cost (0.9486) > electric energy consumption (0.9481) > station area (0.9109) > concrete and cement consumption (0.9032) > other material consumption (0.8831) > gasoline and diesel consumption (0.7258). For the section, the order of influence degree is cost (0.9766) > concrete (0.9581) > steel reinforcement (0.9483) > other steels (0.874) > section length (0.8337) > power energy consumption (0.7169) > wood consumption (0.6684).

Mitigating Curtailment and Carbon Emissions through Load Migration between Data Centers