Associations of individual, household and environmental characteristics with carbon dioxide emissions from motorised passenger travel

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

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.

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 ...

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.

Abstract. Field measurement data on greenhouse gas (GHG) emissions are still scarce for many land-use types in Africa, causing a high level of uncertainty in GHG budgets. To address this gap, we present in situ measurements of carbon dioxide (CO2), nitrous oxide (N2O), and methane (CH4) emissions from the lowlands of southern Kenya. We conducted eight chamber measurement campaigns on gas exchange from four dominant land-use types (LUTs) comprising (1) cropland, (2) bushland, (3) grazing land, and (4) conservation land between 29 November 2017 and 3 November 2018, accounting for regional seasonality (wet and dry seasons and transitions periods). Mean CO2 emissions for the whole observation period were the highest by a significant margin (p value < 0.05) in the conservation land (75±6 mg CO2-C m−2 h−1) compared to the three other sites, which ranged from 45±4 mg CO2-C m−2 h−1 (bushland) to 50±5 mg CO2-C m−2 h−1 (grazing land). Furthermore, CO2 emissions varied between seasons, with significantly higher emissions in the wet season than the dry season. Mean N2O emissions were highest in cropland (2.7±0.6 µg N2O-N m−2 h−1) and lowest in bushland (1.2±0.4 µg N2O-N m−2 h−1) but did not vary with season. In fact, N2O emissions were very low both in the wet and dry seasons, with slightly elevated values during the early days of the wet seasons in all LUTs. On the other hand, CH4 emissions did not show any significant differences across LUTs and seasons. Most CH4 fluxes were below the limit of detection (LOD, ±0.03 mg CH4-C m−2 h−1). We attributed the difference in soil CO2 emissions between the four sites to soil C content, which differed between the sites and was highest in the conservation land. In addition, CO2 and N2O emissions positively correlated with soil moisture, thus an increase in soil moisture led to an increase in emissions. Furthermore, vegetation cover explained the seasonal variation in soil CO2 emissions as depicted by a strong positive correlation between the normalized difference vegetation index (NDVI) and CO2 emissions, most likely because, with more green (active) vegetation cover, higher CO2 emissions occur due to enhanced root respiration compared to drier periods. Soil temperature did not show a clear correlation with either CO2 or N2O emissions, which is likely due to the low variability in soil temperature between seasons and sites. Based on our results, soil C, active vegetation cover, and soil moisture are key drivers of soil GHG emissions in all the tested LUTs in southern Kenya. Our results are within the range of previous GHG flux measurements from soils from various LUTs in other parts of Kenya and contribute to more accurate baseline GHG emission estimates from Africa, which are key to reducing uncertainties in global GHG budgets as well as for informing policymakers when discussing low-emission development strategies.

Elucidating the complex mechanism between urbanization, economic growth, carbon dioxide emissions is fundamental necessary to inform effective strategies on energy saving and emission reduction in China. Based on a balanced panel data of 31 provinces in China over the period 1997-2010, this study empirically examines the relationships among urbanization, economic growth and carbon dioxide (CO2) emissions at the national and regional levels using panel cointegration and vector error correction model and Granger causality tests. Results showed that urbanization, economic growth and CO2 emissions are integrated of order one. Urbanization contributes to economic growth, both of which increase CO2 emissions in China and its eastern, central and western regions. The impact of urbanization on CO2 emissions in the western region was larger than that in the eastern and central regions. But economic growth had a larger impact on CO2 emissions in the eastern region than that in the central and western regions. Panel causality analysis revealed a bidirectional long-run causal relationship among urbanization, economic growth and CO2 emissions, indicating that in the long run, urbanization does have a causal effect on economic growth in China, both of which have causal effect on CO2 emissions. At the regional level, we also found a bidirectional long-run causality between land urbanization and economic growth in eastern and central China. These results demonstrated that it might be difficult for China to pursue carbon emissions reduction policy and to control urban expansion without impeding economic growth in the long run. In the short-run, we observed a unidirectional causation running from land urbanization to CO2 emissions and from economic growth to CO2 emissions in the eastern and central regions. Further investigations revealed an inverted N-shaped relationship between CO2 emissions and economic growth in China, not supporting the environmental Kuznets curve (EKC) hypothesis. Our empirical findings have an important reference value for policy-makers in formulating effective energy saving and emission reduction strategies for China.

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.

Effects of warming and precipitation changes on soil GHG fluxes: A meta-analysis

دراینمقاله،میزانو ارزش انتشارگازهایگلخانه‌ای اکسید‌نیتروس(N2O) و دی‌اکسید‌کربن(CO2)حاصلازتولید حبوبات منتخب ایران (شامل نخود، لوبیا و عدس) با استفاده از مدل GHGE،برایسالزراعی91-90برآورد شده است.نتایج نشان‌داد که استان‌هایفارسوبوشهر، به‌ترتیبباتولیدسالانه271/79 و 004/0 تنN2O، بیشترینوکمترینمیزانتولیدگاز گلخانه‌ایN2Oرا دارامی‌باشند. همچنین استان‌هایلرستانوبوشهر نیز به‌ترتیب باتولیدسالانه83/10327 و33/1‌تنCO2،بیشترینوکمترینمیزانتولیدگاز گلخانه‌ایCO2را به‌خود اختصاص داده‌اند. مجموعهزینه‌هایزیست‌محیطی انتشار گازهای گلخانه‌ای N2O و CO2 کلکشورنیزحدود705/32‌میلیاردریالبرآوردگردید. باتوجهبه یافته‌ها، مدیریت کودهای نیتروژنه مصرفی در مزارعوتوسعهسیاست‌کاهشمیزانانتشاربه‌همراه مالیات زیست‌محیطی انتشار گازهای گلخانه‌ای بر سطوح مختلف تولید پیشنهاد شده ‌است. واژه‌های کلیدی: اکسید‌نیتروس، دی‌اکسید‌کربن، حبوبات، گازهای گلخانه‌ای

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

Peat extraction substantially alters the carbon dynamics, peat structure, and hydrology of peatland sites. In Canada, companies install drainage ditches every ~30 m, dividing the sites into fields of peat bounded by ditches, and remove the surface vegetation and upper acrotelm. Peat is then vacuum harvested, processed, and sold for horticulture use. Despite this disturbance covering only a small percentage of Canadian peatlands, the shift from being a net sink to a net source of carbon during the 15-35 years of extraction makes them an important system to study.We conducted research at eight actively extracted peatland study sites in Quebec (Eastern Canada) and Alberta (Western Canada), ranging from 3–28 years post the start of extraction. Our objectives were to i) assess spatial distribution of CO2 and CH4 emissions; 2) assess seasonal and interannual variability of these emissions; and 3) understand their environmental drivers. To do this, we employed measurement techniques at the plot and ecosystem scale. Plot scale chamber-based measurements of CO2 and CH4 were conducted weekly to biweekly from May to September at eight sites from 2018 to 2022, with each site being measured in at least one study year. The drainage ditches were hotspots of carbon emissions with around double and at least seven times the CO2 and CH4 emissions respectively, of the fields. Time since the start of extraction was a useful metric to estimate current CO2 emissions when sites were within one bog complex. More research will be required to extrapolate emissions to other locations however, as peat substrate quality differences between locations also contributed to variation in carbon loss.Ecosystem scale measurements of daytime March to October CO2 and CH4 emissions were conducted at a subset of the study sites for two to three years using the eddy covariance technique. We observed comparable March and April CO2 emissions to those in July, highlighting the importance of thaw dynamics on the yearly carbon budget. Interannually, CO2 emissions were lowest during a dry summer, suggesting a moisture limitation for decomposition at the surface under severe drainage. We found weak dependence of CO2 emissions on soil temperature, though it was strongest when the water table was within the top 40 cm of the peat.This research will aid in validating Canada’s emission factor values for peat extraction, which are currently based on a few measurements in Quebec at post extracted, unrestored peatlands. Using several different assumptions for wintertime emissions, we estimated annual CO2 budget of 256 – 385 g C m-2 yr-1, which agrees with Canada’s current Tier 2 emission factor value of 310 g C m-2 yr-1. Methane emissions accounted for < 1 g C m-2 yr-1. This research will also support process-based models looking at the effect of site management, and the changing climate, on carbon emissions from these sites.

Is the long-run relationship between economic growth, electricity consumption, carbon dioxide emissions and financial development in Gulf Cooperation Council Countries robust?

Carbon dioxide (CO2) emissions result from human activities like burning fossil fuels. CO2 is a greenhouse gas, contributing to global warming and climate change. Efforts to reduce CO2 emissions include transitioning to renewable energy. Monitoring and reducing CO2 emissions are crucial for mitigating climate change. Strategies include energy efficiency and renewable energy adoption. In the past few decades, several nations have experienced air pollution and environmental difficulties because of carbon dioxide (CO2) emissions. One of the most crucial methods for regulating and maximizing CO2 emission reductions is precise forecasting. Four machine learning algorithms with high forecasting precision and low data requirements were developed in this study to estimate CO2 emissions in the United States (US). Data from a dataset covering the years 1973/01 to 2022/07 that included information on different energy sources that had an impact on CO2 emissions were examined. Then, four algorithms performed the CO2 emissions forecast from the layer recurrent neural network with 10 nodes (L-RNN), a feed-forward neural network with 10 nodes (FFNN), a convolutional neural network with two layers with 10 and 5 filters (CNN1), and convolutional neural network with two layers and with 50 and 25 filters (CNN2) models. Each algorithm's forecast accuracy was assessed using eight indicators. The three preprocessing techniques used are (1) without any processing techniques, (2) processed using max-min normalization technique, and (3) processed using max-min normalization technique and decomposed by variation mode decomposition (VMD) technique with 7 intrinsic mode functions and 1000 iterations. The latter with L-RNN algorithm gave a high accuracy between the forecasting and actual values. The results of CO2 emissions from 2011/05 to 2022/07 have been forecasted, and the L-RNN algorithm had the highest forecast accuracy. The L-RNN model has the lowest value of 1.187028078, 135.5668592, and 11.64331822 for MAPE, MSE, and RMSE, respectively. The L-RNN model provides precise and timely forecasts that can help formulate plans to reduce carbon emissions and contribute to a more sustainable future. Moreover, the results of this investigation can improve our comprehension of the dynamics of carbon dioxide emissions, resulting in better-informed environmental policies and initiatives targeted at lowering carbon emissions.

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.

Strategies toward ambitious climate targets usually rely on the concept of ‘decoupling’; that is, they aim at promoting economic growth while reducing the use of natural resources and GHG emissions. GDP growth coinciding with absolute reductions in emissions or resource use is denoted as ‘absolute decoupling’, as opposed to ‘relative decoupling’, where resource use or emissions increase less so than does GDP. Based on the bibliometric mapping in part I (Wiedenhofer et al, Environ. Res. Lett. 15 063002), we synthesize the evidence emerging from the selected 835 peer-reviewed articles. We evaluate empirical studies of decoupling related to final/useful energy, exergy, use of material resources, as well as CO2 and total GHG emissions. We find that relative decoupling is frequent for material use as well as GHG and CO2 emissions but not for useful exergy, a quality-based measure of energy use. Primary energy can be decoupled from GDP largely to the extent to which the conversion of primary energy to useful exergy is improved. Examples of absolute long-term decoupling are rare, but recently some industrialized countries have decoupled GDP from both production- and, weaklier, consumption-based CO2 emissions. We analyze policies or strategies in the decoupling literature by classifying them into three groups: (1) Green growth, if sufficient reductions of resource use or emissions were deemed possible without altering the growth trajectory. (2) Degrowth, if reductions of resource use or emissions were given priority over GDP growth. (3) Others, e.g. if the role of energy for GDP growth was analyzed without reference to climate change mitigation. We conclude that large rapid absolute reductions of resource use and GHG emissions cannot be achieved through observed decoupling rates, hence decoupling needs to be complemented by sufficiency-oriented strategies and strict enforcement of absolute reduction targets. More research is needed on interdependencies between wellbeing, resources and emissions.