Fossil Fuel Consumption, Carbon Emissions and Temperature Variation in India

Das, Debabrata; Srinivasan, Rajagopalan; Ahmad, Shair

doi:10.1260/0958-305x.22.6.695

Cited by 27 publications

(7 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Akkurt et al (2010) compared three time series models including exponential smoothing method, winters prediction model and Box-Jenkins model and the results showed that exponential smoothing model is the most capable tool for prediction (Akkurt et al , 2010). Das et al (2011) with the assistance of the ARIMA model forecasted the demand for petroleum oil, coal and natural gas in India. They also evaluated the amount of carbon dioxide released by the demand for petroleum oil, natural gas and coal by regression method, and found that carbon dioxide emissions could be reduced by replacing the petroleum oil and coal consumption with natural gas (Das et al , 2011).…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of time series approaches for prediction of energy consumption focusing on greenhouse gases emission in Iran

Doroodi

Mokhtar

2019

IJESM

View full text Add to dashboard Cite

Purpose The purpose of this paper is to predict the amount of energy consumption by using a suitable statistical method in some sectors and energy carriers, which has shown a significant correlation with greenhouse gas emissions. Design/methodology/approach After studying the correlation between energy consumption rates in different sectors of energy consumption and some energy carriers with greenhouse gas distribution (CO2, SO2, NOX and SPM), the most effective factors on pollution emission will be first identified and then predicted for the next 20 years (2015 to 2004). Furthermore, to determine the appropriate method for forecasting, two approaches titled “trend analysis” and “double exponential smoothing” will be applied on data, collected from 1967 to 2014, and their capabilities in anticipating will be compared to each other contributing MSD, MAD, MAPE indices and also the actual and projected time series comparison. After predicting the energy consumption in the sectors and energy carriers, the growth rate of consumption in the next 20 years is also calculated. Findings Correlation study shows that four energy sectors (industry sector, agriculture, transportation and household-general-commercial) and two energy carriers (electricity and natural gas) have shown remarkable correlation with greenhouse gas emissions. To predict the energy consumption in mentioned sectors and carriers, it is proven that double exponential smoothing method is more capable in predicting. The study shows that among the demand sectors, the industry will account for the highest consumption rate. Electricity will experience the highest rate among the energy careers. In fact, producing this amount of electricity causes emissions of greenhouse gases. Research limitations/implications Access to the data and categorized data was one of the main limitations. Practical implications By identifying the sectors and energy carriers that have the highest consumption growth rate in the next 20 years, it can be said that greenhouse gas emissions, which show remarkable correlation with these sectors and carriers, will also increase dramatically. So, their stricter control seems to be necessary. On the other hand, to control a particular greenhouse gas, it is possible to focus on the amount of energy consumed in the sectors and carriers that have a significant correlation with this pollutant. These results will lead to more targeted policies to reduce greenhouse gas emissions. Social implications The tendency of communities toward industrialization along with population growth will doubtlessly lead to more consumption of fossil fuels. An immediate aftermath of burning fuels is greenhouse gas emission resulting in destructive effects on the environment and ecosystems. Identifying the factors affecting the pollutants resulted from consumption of fossil fuels is significant in controlling the emissions. Originality/value Such analyses help policymakers make more informed and targeted decisions to reduce greenhouse gas emissions and make safer and more appropriate policies and investment.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Comparison of time series approaches for prediction of energy consumption focusing on greenhouse gases emission in Iran

Doroodi

Mokhtar

2019

IJESM

View full text Add to dashboard Cite

show abstract

“…Through selecting the global time series data in 161 years and applying the linear method and non-linear method to inspect the casual relationship between CO 2 emissions and air temperature, Bai et al [19] proved that CO 2 emissions are the contributor for the climate warming. Das et al's [20] research suggests that the fossil fuel consumption may cause the CO 2 level to rise to one and half times by the year 2020 in comparison to that of the 2008 level, thus causing an increase in the surface temperature by 0.0008 percent per annum. The above stated research studies have obtained comparatively ideal research achievements.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Variations of CO2 Emission from Energy Consumption in the Yangtze River Delta Region of China and Its Relationship with Nighttime Land Surface Temperature

et al. 2020

View full text Add to dashboard Cite

The rapid development of industrialization and urbanization has resulted in a large amount of carbon dioxide (CO2) emissions, which are closely related to the long-term stability of urban surface temperature and the sustainable development of cities in the future. However, there is still a lack of research on the temporal and spatial changes of CO2 emissions in long-term series and their relationship with land surface temperature. In this study, Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) data, Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) composite data, energy consumption statistics data and nighttime land surface temperature are selected to realize the spatial informatization of long-term series CO2 emissions in the Yangtze River Delta region, which reveals the spatial and temporal dynamic characteristics of CO2 emissions, spatial autocorrelation distribution patterns and their impacts on nighttime land surface temperature. According to the results, CO2 emissions in the Yangtze River Delta region show an obvious upward trend from 2000 to 2017, with an average annual growth rate of 6.26%, but the growth rate is gradually slowing down. In terms of spatial distribution, the CO2 emissions in that region have significant regional differences. Shanghai, Suzhou and their neighboring cities are the main distribution areas with high CO2 emissions and obvious patch distribution patterns. From the perspective of spatial trend, the areas whose CO2 emissions are of significant growth, relatively significant growth and extremely significant growth account for 8.78%, 4.84% and 0.58%, respectively, with a spatial pattern of increase in the east and no big change in the west. From the perspective of spatial autocorrelation, the global spatial autocorrelation index of CO2 emissions in the Yangtze River Delta region in the past 18 years has been greater than 0.66 (p < 0.01), which displays significant positive spatial autocorrelation characteristics, and the spatial agglomeration degree of CO2 emissions continues to increase from 2000 to 2010. From 2000 to 2017, the nighttime land surface temperature in that region showed a warming trend, and the areas where CO2 emissions are positively correlated with nighttime land surface temperature account for 88.98%. The increased CO2 emissions lead to, to a large extent, the rise of nighttime land surface temperature. The research results have important theoretical and practical significance for the Yangtze River Delta region to formulate a regional emission reduction strategy.

show abstract

“…Several research studies have been done in calculating the effects of a single element of solar technologies like PV panels [14][15][16][17], batteries [18], solar-thermal collectors [19], etc. Some others carried out country-based LCA research [20][21][22][23][24][25][26][27][28] to assess the impacts of solar technologies; they have not considered a global database, which is required for the consistent global practice of LCA [29] and global policy development [23]. Moreover, there are separate LCA analyses to find the hazards of solar technologies that directly affect the human body [30][31][32][33], but they have not measured the rate of energy used from fossil-fuel-based sources in manufacturing solar-PV and solar-thermal systems.…”

Section: Introductionmentioning

confidence: 99%

Environmental Impacts of Solar-Photovoltaic and Solar-Thermal Systems with Life-Cycle Assessment

et al. 2018

View full text Add to dashboard Cite

The demand for clean energy is strong, and the shift from fossil-fuel-based energy to environmentally friendly sources is the next step to eradicating the world’s greenhouse gas (GHG) emissions. Solar energy technology has been touted as one of the most promising sources for low-carbon, non-fossil fuel energy production. However, the true potential of solar-based technologies is established by augmenting efficiency through satisfactory environmental performance in relation to other renewable energy systems. This paper presents an environmental life-cycle assessment (LCA) of a solar-photovoltaic (PV) system and a solar-thermal system. Single crystalline Si solar cells are considered for the solar PV system and an evacuated glass tube collector is considered for the solar thermal system in this analysis. A life-cycle inventory (LCI) is developed considering all inputs and outputs to assess and compare the environmental impacts of both systems for 16 impact indicators. LCA has been performed by the International Reference Life Cycle Data System (ILCD), Impact 2002+, Cumulative Energy Demand (CED), Eco-points 97, Eco-indicator 99 and Intergovernmental Panel on Climate Change (IPCC) methods, using SimaPro software. The outcomes reveal that a solar-thermal framework provides more than four times release to air ( 100 % ) than the solar-PV ( 23 . 26 % ), and the outputs by a solar-PV system to soil ( 27 . 48 % ) and solid waste ( 35 . 15 % ) are about one third that of solar-thermal. The findings also depict that the solar panels are responsible for the most impact in the considered systems. Moreover, uncertainty and sensitivity analysis has also been carried out for both frameworks, which reveal that Li-ion batteries and copper-indium-selenium (CIS)-solar collectors perform better than others for most of the considered impact categories. This study revealed that a superior environmental performance can be achieved by both systems through careful selection of the components, taking into account the toxicity aspects, and by minimizing the impacts related to the solar panel, battery and heat storage.

show abstract

Fossil Fuel Consumption, Carbon Emissions and Temperature Variation in India

Cited by 27 publications

References 31 publications

Comparison of time series approaches for prediction of energy consumption focusing on greenhouse gases emission in Iran

Comparison of time series approaches for prediction of energy consumption focusing on greenhouse gases emission in Iran

Spatio-Temporal Variations of CO2 Emission from Energy Consumption in the Yangtze River Delta Region of China and Its Relationship with Nighttime Land Surface Temperature

Environmental Impacts of Solar-Photovoltaic and Solar-Thermal Systems with Life-Cycle Assessment

Contact Info

Product

Resources

About