Optimization of energy consumption and its effect on the energy use efficiency and greenhouse gas emissions of wheat production in Turkey

Imran, Muhammad; Özçatalbaş, Orhan

doi:10.1007/s43621-021-00035-w

Cited by 21 publications

(8 citation statements)

References 47 publications

(60 reference statements)

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“…The unsustainable cluster-the provinces of Isfahan, Kerman, Tehran, Yazd-used more of these energy-equivalent (or energy) inputs. This finding was also reported by other studies in Turkey [49] and Serbia [50], where non-renewable energy represented a greater proportion compared to renewable energy for wheat production.…”

Section: Evaluation Of Energy Indices and Ghg Emissions Across Provin...supporting

confidence: 88%

A Cluster Analysis on the Energy Use Indicators and Carbon Footprint of Irrigated Wheat Cropping Systems

et al. 2022

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The objective of this study is to analyze the energy use efficiency and carbon footprint of irrigated wheat systems in different Iranian provinces. The authors resort to the k-means clustering technique to fulfil the said objective. The empirical results reveal that the average total input energy (59.5 GJ ha−1) is higher than the average energy output (45.82 GJ ha−1) from wheat production, resulting in an average energy efficiency of 0.77, thus rendering the production of irrigated wheat in Iran energy-inefficient on average. Among the thirty wheat-producing Iranian provinces considered in this analysis, only six—East Azerbaijan, Golestan, Ardabil, Kohgiluyeh and Boyer-Ahmad, Alborz, and West Azerbaijan—register an energy use efficiency greater than unity. The average total of greenhouse gas (GHG) emissions from irrigated wheat is 2243.54 kg CO2-eq ha−1 (with electricity and diesel fuel contributing 52.4% and 29.4%, respectively). The authors categorize the clusters into five groups ranging from sustainable to unsustainable. Five of the six provinces referred to earlier fall into the ‘sustainable’ category, with Bushehr being the sixth. The wheat production units in the ‘sustainable’ category can serve as a benchmark for the clusters in the other categories, which can move up the ladder of sustainability. The authors also recommend measures that policymakers can undertake to ensure the sustainable development of wheat production in Iran, fulfilling the social imperative of food self-sufficiency while truncating the environmental footprint and ensuring economic feasibility.

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Section: Evaluation Of Energy Indices and Ghg Emissions Across Provin...supporting

confidence: 88%

A Cluster Analysis on the Energy Use Indicators and Carbon Footprint of Irrigated Wheat Cropping Systems

et al. 2022

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“…Since economic globalisation is strongly related to energy consumption, we added new variable total energy consumption to capture this consequence of economic globalisation. Many studies used energy consumption as an independent variable to capture the impact of economic activity and energy intensity on carbon emissions (Jamil [37], Muhammad and Khan [17], Berrill et al [38], and Imran and Ozcatalbas [39]). Furthermore, environmental taxes are also included to capture the impact of climate-friendly policies and their implementation in the taxation system in the European Union.…”

Section: Methodsmentioning

confidence: 99%

Does Economic Globalisation Harm Climate? New Evidence from European Union

Lenz

Fajdetić

2022

Energies

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The issue of globalisation-induced greenhouse gas emissions is an ongoing topic and a major challenge to the EU climate goals of achieving non-zero emissions by 2050. In the light of this ongoing debate on the globalisation–environment nexus, the paper examines the impact of economic globalisation on climate in EU countries over the period 2000–2019 and provide some new empirical evidence. After applying the panel cointegration analysis and the Granger causality test, the dynamic panel analysis is performed for 26 EU countries using the Arellano–Bond estimator. For the policy perspective, the analysed sample of countries is grouped into two subpanels according to their level of development—EU countries with above-average and below-average GDP per capita. After testing the effects of different dimensions of economic globalisation and environmental taxes on GHG emissions, the results revealed the following: (1) Trade globalisation is detrimental to the climate, as trade openness significantly increases emissions in both country groups. Financial globalisation has a weaker impact and increases emissions only in below-average countries, suggesting that FDI inflows could be important for the transfer of green technologies when a country reaches higher development level. (2) Passenger transport reduces GHG emissions in both groups of countries, while FDI are beneficiary for the climate in above-average countries. (3) Environmental taxes as a proxy for environmental policy show statistically significant results, but with different outcomes in the two groups; they have a negative impact on emissions in countries that are below the GDP p/c average, indicating the shortcomings of the tax system in addressing climate change. (4) The total energy consumption increases emissions in both country groups and, thus, harms the climate. Therefore, despite the current unfavourable circumstances, EU countries should continue to expand the green economy, increase energy consumption from renewables, and develop low-carbon technologies that do not depend on imported fossil fuels.

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“…Direct and indirect energies provide a system boundary of the whole production cycle to quantify environmental impacts, i.e., life cycle assessment, aligned with International Standards Organization (ISO) standards of environmental management (Finkbeiner, 2014b;Finkbeiner, 2014a). The literature on energy use is well documented; numerous studies have been conducted in different regions across the world over different agricultural crops, for instance, energy efficiency assessment of rice (Muazu et al, 2015;Soni and Soe, 2016), wheat (Imran and Ozcatalbas, 2021), corn (Banaeian and Zangeneh, 2011), sugar beet (Kazemi et al, 2015), soybean (Mousavi-Avval et al, 2011c), canola (Mousavi-Avval et al, 2011b, faba bean (Kazemi et al, 2015), alfalfa (Asgharipour et al, 2016), peach and cherry (Aydın and Aktürk, 2018), apple (Çelen et al, 2017), and vegetables (Heidari and Omid, 2011). However, there are few studies on energy input-output of cotton production; Kazemi et al (2018) analyzed the energy use efficiency of cotton cultivations in two climatic regions of Iran (Darab and Gorgan).…”

Section: Introductionmentioning

confidence: 99%

Analysis of Energy Input–Output of Farms and Assessment of Greenhouse Gas Emissions: A Case Study of Cotton Growers

Abbas

Zhao

Waseem

et al. 2022

Front. Environ. Sci.

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The concept of agricultural and environmental sustainability refers to minimizing the degradation of natural resources while increasing crop productions; assessment of inflow and outflow energy resources is helpful in highlighting the resilience of the system and maintaining its productivity. In this regard, the current study evaluated the amount of energy input–output of cotton productions and their environmental interventions. Data are randomly collected from 400 cotton farmers through face-to-face interview. Results suggested that the major energy is consumed by three culprits, i.e., chemical fertilizer, diesel fuel, and irrigation water (11,532.60, 11,121.54, and 4,531.97 MJ ha−1, respectively). Total greenhouse gas (GHG) emission is 1,106.12 kg CO2eq ha−1 with the main share coming from diesel fuel, machinery, and irrigation water. Stimulating data of energies, e.g., energy use efficiency (1.53), specific energy (7.69 MJ kg−1), energy productivity (0.13 kg MJ−1), and net energy gained (16,409.77 MJ ha−1). Further analysis using data envelopment analysis (DEA) showed that low technical efficiency, i.e., 69.02%, is the most probable cause of poor energy use efficiency. The impermanent trend in growth of energy efficiency has been witnessed with plausible potential of energy savings from 4,048.012 to 16,194.77 MJ ha−1 and a reduction of 148.96–595.96 kg CO2eq ha−1 in GHG emission. Cobb–Douglas production function is further applied to discover the associations of energy input to output, which inferred that chemical fertilizer, diesel fuel, machinery, and biocides have significant effect on cotton yield. The marginal physical productivity (MPP) values obliged that the additional use in energy (1 MJ) from fuel (diesel), biocides, and machinery can enhance cotton yield at the rate of 0.35, 1.52, and 0.45 kg ha−1, respectively. Energy saving best links with energy sharing data, i.e., 55.66% (direct), 44.34% (indirect), 21.05% (renewable), and 78.95% (nonrenewable), further unveiled the high usage of nonrenewable energy resources (fossil fuels) that ultimately contributes to high emissions of GHGs. We hope that these findings could help in the management of energy budget that we believe will reduce the high emissions of GHGs.

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Optimization of energy consumption and its effect on the energy use efficiency and greenhouse gas emissions of wheat production in Turkey

Cited by 21 publications

References 47 publications

A Cluster Analysis on the Energy Use Indicators and Carbon Footprint of Irrigated Wheat Cropping Systems

A Cluster Analysis on the Energy Use Indicators and Carbon Footprint of Irrigated Wheat Cropping Systems

Does Economic Globalisation Harm Climate? New Evidence from European Union

Analysis of Energy Input–Output of Farms and Assessment of Greenhouse Gas Emissions: A Case Study of Cotton Growers

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