The impact of climate change on global energy use

Zhang, Hongliang; Mu, Jianhong E.; McCarl, Bruce A.; Yu, Jialing

doi:10.1007/s11027-021-09986-x

Cited by 12 publications

(3 citation statements)

References 35 publications

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“…Since the first industrial revolution, the massive consumption of fossil fuels has promoted the innovation of modern science and technology and greatly raised the quality of human lifestyles [ 1 , 2 , 3 , 4 , 5 ]. However, the challenges of energy scarcity and atmospheric pollution are in irreversible progress, and it is imperative to formulate a clean and sustainable energy technology route [ 6 , 7 , 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Asymmetric Coordination Environment Engineering of Atomic Catalysts for CO2 Reduction

et al. 2023

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Single-atom catalysts (SACs) have emerged as well-known catalysts in renewable energy storage and conversion systems. Several supports have been developed for stabilizing single-atom catalytic sites, e.g., organic-, metal-, and carbonaceous matrices. Noticeably, the metal species and their local atomic coordination environments have a strong influence on the electrocatalytic capabilities of metal atom active centers. In particular, asymmetric atom electrocatalysts exhibit unique properties and an unexpected carbon dioxide reduction reaction (CO2RR) performance different from those of traditional metal-N4 sites. This review summarizes the recent development of asymmetric atom sites for the CO2RR with emphasis on the coordination structure regulation strategies and their effects on CO2RR performance. Ultimately, several scientific possibilities are proffered with the aim of further expanding and deepening the advancement of asymmetric atom electrocatalysts for the CO2RR.

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Section: Introductionmentioning

confidence: 99%

Asymmetric Coordination Environment Engineering of Atomic Catalysts for CO2 Reduction

et al. 2023

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“…Typically reported as carbon dioxide equivalent (CO2e), nitrogen oxide (N 2 O), methane (CH 4 ), and carbon dioxide (CO 2 ) are the principal GHG emissions from automobiles [9]. Globally, transportation activities produce 75% of total environmental CO 2 emissions [10]. Consequently, the reduction of emissions from transportation is a worldwide objective against climate change [11].…”

Section: Introductionmentioning

confidence: 99%

Predicting CO2 Emissions from Traffic Vehicles for Sustainable and Smart Environment Using a Deep Learning Model

Al-Nefaie

Aldhyani

2023

Sustainability

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Burning fossil fuels results in emissions of carbon dioxide (CO2), which significantly contributes to atmospheric changes and climate disturbances. Consequently, people are becoming concerned about the state of the environment, and governments are required to produce precise projections to develop efficient preventive measures. This study makes a significant contribution to the area by modeling and predicting the CO2 emissions of vehicles using advanced artificial intelligence. The model was constructed using the CO2 emission by vehicles dataset from Kaggle, which includes different parameters, namely, vehicle class, engine size (L), cylinder transmission, fuel type, fuel consumption city (L/100 km), fuel consumption hwy (L/100 km), fuel consumption comb (L/100 km), fuel consumption comb (mpg), and CO2 emissions (g/km). To forecast the CO2 emissions produced by vehicles, a deep learning long short-term memory network (LSTM) model and a bidirectional LSTM (BiLSTM) model were developed. Both models are efficient. Throughout the course of the investigation, the researchers employed four statistical assessment metrics: the mean square error (MSE), the root MSE (RMSE), Pearson’s correlation coefficient (R%), and the determination coefficient (R2). Based on the datasets of experiments carried out by Kaggle, the LSTM and BiLSTM models were created and implemented. The data were arbitrarily split into two phases: training, which included 80% of the total data, and testing, which comprised 20% of the total data. The BiLSTM model performed best in terms of accuracy and achieved high prediction values for MSE and RMSE. The BiLSTM model has the greatest attainable (R2 = 93.78). In addition, R% was used to locate a connection between the dataset’s characteristics to ascertain which characteristics had the highest level of association with CO2 emissions. An original strategy for the accurate forecasting of carbon emissions was developed as a result of this work. Consequently, policymakers may use this work as a potentially beneficial decision-support tool to create and execute successful environmental policies.

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“…Humanity faces the problem of balancing economic development with ecological conservation. The Paris agreement signed in 2016 proposed to limit the rise in global average temperature to 2 • C compared to the pre-industrial period [1]. On 22 September 2020, President Xi Jinping announced that "China will increase its autonomous national contribution, adopt more vigorous policies and measures, and strive to peak carbon emissions by 2030 and achieve carbon neutrality by 2060".…”

Section: Introductionmentioning

confidence: 99%

Predicting the Potential of China’s Geothermal Energy in Industrial Development and Carbon Emission Reduction

et al. 2023

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The goal of carbon peaking and carbon neutrality requires major systemic changes in the energy supply sector. As one of the major non-carbon-based energy sources, geothermal energy is characterized by large reserves, stability, and reliability. This paper summarizes the current situation of geothermal resource endowment and industrial development in China. Based on this, a system dynamics model of geothermal industrialization is established, and the potential of geothermal industrialization and carbon emission reduction in China is predicted. The prediction results show that the growth rate of geothermal heating and cooling areas in the next 40 years will follow a trend of acceleration followed by deceleration. China’s geothermal energy heating and cooling area will reach 11.32–14.68 billion m2 by 2060, an increase of about 9–12 times compared to 2020. The proportion of geothermal heating and cooling area to the total building area in China will reach 13.77–17.85%. The installed capacity of geothermal power generation will reach 14,452.80–20,963.20 MW by 2060 under the scenario with electricity subsidies. The proportion of geothermal energy in China’s primary energy consumption structure will reach 3.67–5.64%. The annual carbon emission reduction potential of the geothermal industry will reach 436–632 million tons, equivalent to 4.41–6.39% of China’s carbon emissions in 2020. The results of this study can provide a reference for the healthy and high-quality development of China’s geothermal industry and help to achieve carbon peaking and carbon neutrality goals.

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The impact of climate change on global energy use

Cited by 12 publications

References 35 publications

Asymmetric Coordination Environment Engineering of Atomic Catalysts for CO2 Reduction

Asymmetric Coordination Environment Engineering of Atomic Catalysts for CO2 Reduction

Predicting CO2 Emissions from Traffic Vehicles for Sustainable and Smart Environment Using a Deep Learning Model

Predicting the Potential of China’s Geothermal Energy in Industrial Development and Carbon Emission Reduction

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