A projection of energy consumption and carbon dioxide emissions in the electricity sector for Saudi Arabia: The case for carbon capture and storage and solar photovoltaics

BackgroundDigital health and telemedicine are potentially important strategies to decrease health care’s environmental impact and contribution to climate change by reducing transportation-related air pollution and greenhouse gas emissions. However, we currently lack robust national estimates of emissions savings attributable to telemedicine.ObjectiveThis study aimed to (1) determine the travel distance between participants in US telemedicine sessions and (2) estimate the net reduction in carbon dioxide (CO2) emissions attributable to telemedicine in the United States, based on national observational data describing the geographical characteristics of telemedicine session participants.MethodsWe conducted a retrospective observational study of telemedicine sessions in the United States between January 1, 2022, and February 21, 2023, on the doxy.me platform. Using Google Distance Matrix, we determined the median travel distance between participating providers and patients for a proportional sample of sessions. Further, based on the best available public data, we estimated the total annual emissions costs and savings attributable to telemedicine in the United States.ResultsThe median round trip travel distance between patients and providers was 49 (IQR 21-145) miles. The median CO2 emissions savings per telemedicine session was 20 (IQR 8-59) kg CO2). Accounting for the energy costs of telemedicine and US transportation patterns, among other factors, we estimate that the use of telemedicine in the United States during the years 2021-2022 resulted in approximate annual CO2 emissions savings of 1,443,800 metric tons.ConclusionsThese estimates of travel distance and telemedicine-associated CO2 emissions costs and savings, based on national data, indicate that telemedicine may be an important strategy in reducing the health care sector’s carbon footprint.

Energy-related activities are a major contributor of greenhouse gas (GHG) emissions. A growing body of knowledge clearly depicts the links between human activities and climate change. Over the last century the burning of fossil fuels such as coal and oil and other human activities has released carbon dioxide (CO2) emissions and other heat-trapping GHG emissions into the atmosphere and thus increased the concentration of atmospheric CO2 emissions. The main human activities that emit CO2 emissions are (1) the combustion of fossil fuels to generate electricity, accounting for about 37% of total U.S. CO2 emissions and 31% of total U.S. GHG emissions in 2013, (2) the combustion of fossil fuels such as gasoline and diesel to transport people and goods, accounting for about 31% of total U.S. CO2 emissions and 26% of total U.S. GHG emissions in 2013, and (3) industrial processes such as the production and consumption of minerals and chemicals, accounting for about 15% of total U.S. CO2 emissions and 12% of total ...

The problem of climate change, which causes various negativities in the global sense, is one of the important research topics. It is necessary to determine the factors affecting carbon dioxide (CO2) emissions, which are the main determinants of climate change, and to take measures for this. In this study, based on the hypothesis that economic growth, energy usage, trade openness, and foreign direct investment affect CO2 emissions, it was aimed to examine the effects of economic growth, energy usage, trade openness, and foreign direct investment on CO2 emissions for G8 countries using annual data for the period 1990-2018. For this purpose, first, a literature review was done in the study. Then, cross-section dependency and heterogeneity tests were performed as empirical analyses. Afterward, unit root tests, cointegration analyses, and causality analyses were performed in the study. Finally, in the study, short-term parameters and long-term parameters were estimated to capture possible dynamic relationships between variables. The Westerlund Error Correction Model (ECM) panel test for cointegration showed that there is a cointegration relationship between these variables for both the entire panel and the cross-section units. The results of the Augmented Mean Group (AMG) estimator method showed that (i) economic growth has no effect on CO2 emissions in 7 of 8 countries, (ii) energy usage increases CO2 emissions in 4 of the countries studied but decreases it in one of them, and (iii) foreign direct investments and trade openness do not affect CO2 emissions in 4 countries but positively affects in 2 countries and negatively in 2 countries. According to the results obtained from the Pooled Mean Group (PMG) analysis, while economic growth, energy usage, and trade openness affect CO2 emissions in the long run, economic growth, energy use, and trade openness affect CO2 emissions in the short run too. According to Dumitrescu-Hurlin panel causality results, it was seen that there is no causal relationship between CO2 emissions, economic growth, and energy use. While there is a unidirectional causality from CO2 emissions to foreign direct investments, it was determined that there is a bidirectional causality between trade openness and CO2 emissions. When the results were examined in general, it was understood that the variables of economic growth, trade openness, foreign direct investment, and energy usage are effective on CO2 emissions in the G8 countries. It would be beneficial for countries to include the objectives of making production with clean production technologies, ensuring efficient use of energy, and expanding the use of renewable energies among their main targets.

Low-carbon production of iron and steel: Technology options, economic assessment, and policy

Water scarcity is a global challenge, especially in arid regions, including Middle Eastern and North African countries. The distribution of water around the earth is not even. Trading water in the form of an embedded commodity, known as the water footprint (WF), from water-abundant regions to water-scarce regions, is a viable solution to water scarcity problems. Agricultural products account for approximately 85% of the earth’s total WF, indicating that importing water-intense crops, such as cereal crops, can partially solve the local water scarcity problem. This study investigated water, energy, and food nexus dynamics for the trades of a few major crops, specifically considering Saudi Arabia. It analyzed the trade of crops and its impact on WF, energy, and carbon dioxide (CO2) emission savings. The findings revealed that importing major cereal crops to Saudi Arabia could significantly reduce the local WF. The imports of wheat, maize, rice, and barley reduced approximately 24 billion m3 per year of consumable WF (i.e., blue and green water footprint) in the global scale. Similarly, the trade of major crops had a significant impact on energy and CO2 emission savings. The energy savings from the wheat, maize, and barley trades in Saudi Arabia was estimated to be approximately 9 billion kWh. It also saved about 7 million tons per year of CO2 emissions. The trades of cereal crops in Saudi Arabia reduced water consumption, energy usage, and CO2 emissions significantly.

Net-zero emissions chemical industry in a world of limited resources

Summary Cities, contributing more than 75% of global carbon emissions, are at the heart of climate change mitigation. Given cities' heterogeneity, they need specific low-carbon roadmaps instead of one-size-fits-all approaches. Here, we present the most detailed and up-to-date accounts of CO2 emissions for 294 cities in China and examine the extent to which their economic growth was decoupled from emissions. Results show that from 2005 to 2015, only 11% of cities exhibited strong decoupling, whereas 65.6% showed weak decoupling, and 23.4% showed no decoupling. We attribute the economic-emission decoupling in cities to several socioeconomic factors (i.e., structure and size of the economy, emission intensity, and population size) and find that the decline in emission intensity via improvement in production and carbon efficiency (e.g., decarbonizing the energy mix via building a renewable energy system) is the most important one. The experience and status quo of carbon emissions and emission-GDP (gross domestic product) decoupling in Chinese cities may have implications for other developing economies to design low-carbon development pathways.

A dynamic STIRPAT model used in the current study is based on panel data from the eight most populous countries from 1975 to 2020, revealing the nonlinear effects of urbanization routes (percentage of total urbanization, percentage of small cities and percentage of large cities) on carbon dioxide (CO2) emissions. Using “Dynamic Display Unrelated Regression (DSUR)” and “Fully Modified Ordinary Least Squares (FMOLS)” regressions, the outcomes reflect that percentage of total urbanization and percentage of small cities have an incremental influence on carbon dioxide emissions. However, square percentage of small cities and square percentage of total urbanization have significant adverse effects on carbon dioxide (CO2) emissions. The positive relationship between the percentage of small cities, percentage of total urbanization and CO2 emissions and the negative relationship between the square percentage of small cities, square percentage of total urbanization and CO2 emissions legitimize the inverted U-shaped EKC hypothesis. The impact of the percentage of large cities on carbon dioxide emissions is significantly negative, while the impact of the square percentage of large cities on carbon dioxide emissions is significantly positive, validating a U-shaped EKC hypothesis. The incremental effect of percentage of small cities and percentage of total urbanization on long-term environmental degradation can provide support for ecological modernization theory. Energy intensity, Gross Domestic Product (GDP), industrial growth and transport infrastructure stimulate long-term CO2 emissions. Country-level findings from the AMG estimator support a U-shaped link between the percentage of small cities and CO2 emissions for each country in the entire panel except the United States. In addition, the Dumitrescu and Hulin causality tests yield a two-way causality between emission of carbon dioxide and squared percentage of total urbanization, between the percentage of the large cities and emission of carbon dioxide, and between energy intensity and emission of carbon dioxide. This study proposes renewable energy options and green city-friendly technologies to improve the environmental quality of urban areas.

CO2 Emissions Savings Produced by the Construction of an Upgraded Freight Rail Corridor. Application to Extremadura

Population growth and trends are centrally important to the environment because it helps to determine the environmental impact of human activities. In this study, the World Bank database has been used. Here, carbon dioxide (CO2) emissions, and energy intensity (EI) are considered as environmental indicators. The population indicators are the proportion of the population aged 15-64 years, and the percentage of the urban population. The Gross Domestic Product (GDP) is considered a development indicator in a country. This study tries to identify the association between population environment and development. Correlation analysis has been employed to know association and Path analysis is used to determine the important factors for environmental impacts such as carbon dioxide (CO2) emissions. The result presents that the zero-order correlation exists among energy intensity (EI), the proportion of the population aged 15-64 (P15-64), urbanization (UR), gross domestic product (GDP) per capita (US$), total population (P) ) and carbon dioxide (CO2) emission in Bangladesh and India. It is observed that 8 paths for Bangladesh and 7 paths for India out of each 12 hypothesized paths are found to be statistically significant. In Bangladesh, the total effects of exogenous variables like as energy intensity (X1) and population aged 15-64 (X2) are observed negative direction on carbon dioxide emissions (X6) and the remaining variable like as urbanization (X3) is observed as positive direction on carbon dioxide emissions. However, in India total effects of these two exogenous variables population aged 15-64 (X2) and urbanization (X3) are observed positive direction on carbon dioxide emissions (X6) and the remaining variable like as energy intensity (X1) is observed negative direction on carbon dioxide emissions (X6). The total effects of endogenous variables like as GDP per capita (X4) show a negative direction on carbon dioxide emissions and population (X5) shows a positive direction on carbon dioxide emissions. The study demonstrates that CO2 emission is important for environmental impact in Bangladesh and India. There is a strong association between population, GDP per capita, energy consumption and urbanization and CO2 emission in Bangladesh and India. The factors of CO2 emissions play an important role in environmental degradation. Thus, attention should be focused on using low energy consumption, and proper urbanization, particularly on modern technology which assures fewer uses of CO2 emissions in Bangladesh and India.

The impact of energy consumption, income and foreign direct investment on carbon dioxide emissions in Vietnam

This paper empirically verifies the validity of the Environmental Kuznets Curve (EKC) hypothesis in Nigeria by focusing on the relationship between key sectors of the Nigerian economy and environmental degradation. The study adopted the Autoregressive Distributed Lag (ARDL) model using time series data for the period 1981-2018. The bounds-testing approach proposed by Pesaran, Shin and Smith, (2001) was adopted to test for cointegration. The results show a long-run relationship between economic growth (disaggregated into key sectors) and environmental degradation measured by carbon dioxide emissions. In the short run, agriculture, industry and services sectors significantly explained the variation in carbon dioxide (CO2) emissions, while the construction sector does not have any significant effect on Carbon emissions both in the current and the future periods. Specifically, the industrial sector has a positive effect on CO2 emissions which confirms the short-run EKC hypothesis; while agriculture and services though were significant, have a negative effect on CO2 emissions, invalidating the short-run EKC hypothesis. In the long run, industry and services sectors were significant in explaining variation in CO2 emission. However, while the services sector shows a negative relationship with CO2 emission in line with the long-run EKC Hypothesis, the industrial sector invalidates the hypothesis with a positive effect. These results imply that the key sectors of the economy have varied effects on environmental degradation, hence the hypothesis is inconclusive. Nigeria is therefore advised to pursue economic growth via industrial and services sectors with an emphasis on environmental sustainability, which could be achieved through the use of renewable and cleaner technology in nation-building.

Singapore is a foremost tourist destination country experiencing continuous economic growth and rapid urbanization which is causing higher energy consumption and carbon dioxide (CO2) emissions. This study aims to investigate the dynamic impacts of economic growth, energy use, urbanization, and tourism on CO2 emissions in Singapore. Time series data from 1990 to 2019 were utilized by employing the dynamic ordinary least squares (DOLS) approach. The DOLS findings show that the long-run coefficient of economic growth is negative and significant, indicating that a 1% rise in economic growth will result in a 0.99% reduction in CO2 emissions. Furthermore, the coefficient of energy use is positive and significant which reveals that an increasing 1% of energy use is linked with a rising of 0.52% CO2 emissions in the long run. In addition, the long-run coefficient of urbanization is positive and significant, implying that rising urbanization by 1% causes a 1.90% increase in CO2 emissions. Moreover, the coefficient of tourism is positive and significant, which specifies that an increase in tourism activities by 1% is associated with a 0.45% increase in CO2 emissions in the long run. The estimated results are robust to alternative estimators such as ordinary least squares (OLS), fully modified least squares (FMOLS), and canonical cointegrating regression (CCR). Furthermore, the pairwise Granger causality test was utilized to capture the causal linkage between the variables. This article put forward policy recommendations toward environmental sustainability by establishing strong regulatory policy instruments to reduce environmental degradation.

Measuring regional efficiency of energy and carbon dioxide emissions in China: A chance constrained DEA approach

Accurately measuring carbon dioxide (CO2) emissions is critical for effectively implementing carbon reduction policies, and China’s increased investment in reducing CO2 emissions is expected to significantly impact the world. In this study, the potential of shallow learning for predicting CO2 emissions was explored. Data included CO2 emissions, renewable energy consumption, and the share of primary, secondary, and tertiary industries in China from 1965 to 2021. These time-series data were converted into labeled sample data using the sliding window method to facilitate a supervised learning model for CO2 emission prediction. Then, different shallow learning models with k-fold cross-validation were used to predict China’s short-term CO2 emissions. Finally, optimal models were presented, and the important features were identified. The key findings were as follows. (1) The combined model of RF and LASSO performed best at predicting China’s short-term CO2 emissions, followed by LASSO and SVR. The prediction performance of RF was very fragile to the window width. (2) The sliding window method is used to convert time series predictions into supervision learning problems, and historical data can be used to predict future carbon dioxide emissions. To ensure that the feature data are real, the model can predict CO2 emissions for up to six years ahead. (3) Cross-validation and grid search were critical for optimizing China’s CO2 emissions prediction with small datasets. (4) By 2027, carbon dioxide emissions will continue to grow and reach 10.3 billion tons. It can be seen that the task of China to achieve its carbon peak on schedule is very heavy. The results indicate that an increase in renewable energy consumption and adjustments in industrial structure will continue to play an important role in curbing China’s CO2 emissions.