Explore the vast range of titles available.

The CO 2 emission factor is the basis for analyzing vehicle CO 2 emissions. This study establishes a correlation model between the fuel CO 2 emission factor and the mileage-based CO 2 emission factor using fuel consumption data, then analyzes the fuel consumption and CO 2 emission situation of vehicles in Beijing with the established models. The main research conclusions are as follows: The proposed correlation models are effective for analyzing urban vehicle CO 2 emissions. Cars can only meet the national standard limit when traveling at an average speed of 42.17 km/h. During rush hours, fuel consumption in most cities in China exceeds national standards, making it urgent to improve urban traffic efficiency. Due to the decline in urban traffic conditions in Beijing in 2023 (average speed decreased from 29.14 km/h to 24.26 km/h), each passenger car consumes an average of 0.282 L more gasoline per day, emitting an additional 619.87 g of CO 2 . This study is of great significance for energy conservation and emission reduction in road transportation.

A large fraction of the organic carbon derived from land that is transported through inland waters is decomposed along river systems and emitted to the atmosphere as carbon dioxide (CO2). The Amazon River outgasses nearly as much CO2 as the rainforest sequesters on an annual basis, representing ~25% of global CO2 emissions from inland waters. However, current estimates of CO2 outgassing from the Amazon basin are based on a conservative upscaling of measurements made in the central Amazon, meaning both basin and global scale budgets are likely underestimated. The lower Amazon River, from Óbidos to the river mouth, represents ~13% of the total drainage basin area, and is not included in current basin-scale estimates. Here, we assessed the concentration and evasion rate of CO2 along the lower Amazon River corridor and its major tributaries, the Tapajós and Xingu Rivers. Evasive CO2 fluxes were directly measured using floating chambers and gas transfer coefficients (k600) were calculated for different hydrological seasons. Temporal variations in pCO2 and CO2 emissions were similar to previous observations throughout the Amazon (e.g. peak concentrations at high water) and CO2 outgassing was lower in the clearwater tributaries compared to the mainstem. However, k600 values were higher than previously reported upstream likely due to the generally windier conditions, turbulence caused by tidal forces, and an amplification of these factors in the wider channels with a longer fetch. We estimate that the lower Amazon River mainstem emits 0.2 Pg C yr-1 within our study boundaries, or as much as 0.48 Pg C yr-1 if the entire spatial extent to the geographical mouth is considered. Including these values with updated basin scale estimates and estimates of CO2 outgassing from small streams we estimate that the Amazon running waters outgasses as much as 1.39 Pg C yr-1, increasing the global emissions from inland waters by 43% for a total of 2.9 Pg C yr-1. These results highlight a large missing gap in basin-scale carbon budgets along the complete continuum of the Amazon River, and likely most other large river systems, that could drastically alter global scale carbon budgets.

The objective of the paper is to figure out the nexus between renewable energy consumption and sustainable economic development for emerging and developing countries. In this paper, a panel of 30 emerging and developing countries is selected using the World Development Indicators (WDI) of the World Bank, Renewable Energy Country Attractiveness Index (RECAI) by Ernst and Young, and a random selection method based on the current trend of renewable energy consumption for five different regions of the world i.e., Asia, South-Asia, Latin America, Africa and the Caribbean. To achieve the objective, robust panel econometric models such as the Pesaran cross-section dependence (CD) test, second generation panel unit root test, e.g., cross-sectional augmented IPS test (CIPS) proposed by Pesran (2007), panel co-integration test, fully modified ordinary least square (FMOLS) and dynamic ordinary least square (DOLS) are applied to check the cross-sectional dependence, heterogeneity and long-term relationship among variables. The panel is strongly balanced and the findings suggest a significant long-run relationship between renewable energy consumption and economic growth for selected South Asian, Asian and most of the African countries (Ghana, Tunisia, South Africa, Zimbabwe and Cameroon). But for the Latin American and the Caribbean countries, economic growth depends on non-renewable energy consumption. Renewable energy consumption in the selected countries of these two regions are still at the initial stage. In case of the renewable energy consumption and CO 2 emissions nexus, for selected South Asian, Asian, Latin American and African countries both GDP and non-renewable energy consumption cause the increase of CO 2 emissions. For the Caribbean countries only non-renewable energy consumption causes the increase of CO 2 emissions. An important finding regarding renewable energy consumption-economic growth nexus indicates the existence of bi-directional causality. This supports the existence of a feedback hypothesis for the emerging and developing economies. In the case of renewable energy consumption- CO 2 emissions nexus, there exists unidirectional causality. This supports the existence of the conservation hypothesis, where CO 2 emissions necessitates the renewable energy consumptions. Based on the findings, the study proposes possible policy options. The countries, who have passed the take-off stage of renewable energy consumption, can take advanced policy initiatives e.g., feed-in tariff, renewable portfolio standard and green certificate for long-term economic development. Other countries can undertake subsidy, low interest loan and market development to facilitate the renewable energy investments.

Southwestern North America (SWNA) continuously experienced megadroughts and large wildfires in 2020 and 2021. Here, we quantified their impact on the terrestrial carbon budget using net biome production (NBP) estimates from an ensemble of atmospheric inversions assimilating in-situ CO2 and Carbon Observatory–2 (OCO-2) satellite XCO2 retrievals (OCO-2 v10 MIP Extension), two satellite-based gross primary production (GPP) datasets, and two fire CO2 emission datasets. We found that the 2020–2021 drought and associated wildfires in SWNA led to a large CO2 loss, an ensemble mean of 95.07 TgC estimated by the satellite inversions using both nadir and glint XCO2 retrievals (LNLG) within the OCO-2 v10 MIP, greater than 80% of SWNA’s annual total carbon sink. Moreover, the carbon loss in 2020 was mainly contributed by fire emissions while in 2021 mainly contributed by drought impacts on terrestrial carbon uptake. In addition, the satellite inversions indicated the huge carbon loss was mainly contributed by fire emissions from forests and grasslands along with carbon uptake reductions due to drought impacts on grasslands and shrublands. This study provides a process understanding of how some droughts and following wildfires affect the terrestrial carbon budget on a regional scale.

The intensification of global climate change and the resulting environmental challenges have made carbon emission control a focal point of global attention. As one of the major sources of carbon emissions, the power sector plays a critical role in accurately quantifying CO2 emissions, which is essential for formulating effective emission reduction policies and action plans. The average CO2 emission factor of electricity (AEF), as a key parameter, is widely used in calculating indirect carbon emissions from purchased electricity in various industries. The International Energy Agency (IEA) reported an AEF of 0.6093 kgCO2/kWh for China in 2021, while the Ministry of Ecology and Environment of China (MEE) officially reported a value of 0.5568 kg CO2/kWh, resulting in a discrepancy of 9.43%. This study conducts an in-depth analysis of the calculation methodologies used by the MEE and IEA, comparing them from two critical dimensions: calculation formulas and data sources, to explore potential causes of the observed discrepancies. Differences in formula components include factors such as electricity trade, the allocation of emissions from combined heat and power (CHP) plants, and emissions from own energy use in power plants. Notably, the IEA’s inclusion of CHP allocation reduces its calculated emissions by 10.99%. Regarding data sources, this study focuses on total carbon emissions and total electricity generation, revealing that the IEA’s total carbon emissions exceed those of the MEE by 9.71%. This exploratory analysis of the discrepancies in China’s AEFs provides valuable insights and a foundational basis for further research.

It is widely discussed that GDP growth has a vague impact on environmental pollution due to carbon dioxide emissions from fossil fuels consumed in production, transportation, and power generation. The main purpose of this study is to investigate the relationships between economic growth, fossil fuel consumption, mortality (from cardiovascular disease (CVD), diabetes mellitus (DM), cancer, and chronic respiratory disease (CRD), and environmental pollution since environmental pollution can be a reason for societal mortality rate increases. This study uses the generalized method of moments (GMM) estimation technique for the Commonwealth of Independent States (CIS) members for the period from 1993–2018. The major results revealed that the highest variability of mortality could be explained by CO2 variability. Regarding fossil fuel consumption, the estimation proved that this variable positively affects mortality from CVD, DM, cancer, and CRD. Additionally, any improvements in the human development index (HDI) have a negative effect on mortality increases from CVD, DM, cancer, and CRD in the CIS region. It is recommended that the CIS members implement different policies to improve energy transitions, indicating movement from fossil fuel energy sources to renewable sources. Moreover, we recommend the CIS members enhance various policies for easy access to electricity from green sources and increase the renewable supply through improved technologies, sustainable economic growth, and increase the use of green sources in daily social life.

Accelerating the green transition of the economy is an effective way to conserve energy and reduce emissions, and its impact on the greenhouse effect deserves in-depth discussion. Based on this, we examine the potential effect of China's green growth on carbon dioxide (CO 2 ) emissions by applying provincial panel data from 2004 to 2018. The regional heterogeneity and how does green finance affect the green growth-CO 2 nexus are also checked. The primary findings imply that: (i) China's green growth achieves preliminary results, and its impact on CO 2 emissions is significantly negative. Also, green finance can facilitate carbon emission reduction; (ii) significant regional heterogeneity exists within various regions. Only in the central and western regions can green growth effectively reduce CO 2 emissions, and in the eastern and central regions, green finance is conducive to promoting carbon reduction; and (iii) the mediating role of green finance is significant. In other words, China's green growth not only mitigates the greenhouse effect directly, but also affects CO 2 emissions indirectly by accelerating the development of green finance.

This study investigates the feasibility of rice straw for energy production in Cambodia. The potential areas for a 10 MW biomass-fired power plant installation are estimated based on rice straw availability displayed in a graphic information system (GIS). The discounted cash flow (DCF) method on the profitability index (PI) was executed by Mathlab software, which was used to determine the period of the power plant profitability. The reduction of CO and CO2 emissions from the proposed rice straw biomass-fired power plant with 10 MW capacity was calculated and compared with the coal-fired power plant and open field burning. Prey Veng, Takeo, and Battambang are potential provinces that have an estimated rice straw source of 804,796 t/annum, 720,040 t/annum, and 603,273 t/annum, respectively. Within a 20-year project, the biomass-fired power plant can reach profitability between six and ten years with the operation of the rice-straw price of 20 USD/t to 40 USD/t. The total energy produced by these potential areas is 1251 GWh/annum, with a CO2 emission avoidance of 1.06 million t/annum compared to the coal-fired power plant operation. Simultaneously, the emission savings of the biomass-fired power plant compared to open-field burning are 0.61 million t/annum of CO2 and 0.02 million t/annum of CO in the study site. The findings are prospectively essential for further designing of a small-scale biomass-fired power plant in Cambodia.

Development of technology and economy is often accompanied by surging usage of fossil fuels. Global warming could speed up air pollution and cause floods and droughts, not only affecting the safety of human beings, but also causing drastic economic changes. Therefore, the trend of carbon dioxide emissions and the factors affecting growth of emissions have drawn a lot of attention in all countries in the world. Related studies have investigated many factors that affect carbon emissions such as fuel consumption, transport emissions, and national population. However, most of previous studies on forecasting carbon emissions hardly considered more than two factors. In addition, conventional statistical methods of forecasting carbon emissions usually require some assumptions and limitations such as normal distribution and large dataset. Consequently, this study proposes a two-stage forecasting approach consisting of multivariable grey forecasting model and genetic programming. The multivariable grey forecasting model at the first stage enjoys the advantage of introducing multiple factors into the forecasting model, and can accurately make prediction with only four or more samples. However, grey forecasting may perform worse when the data is nonlinear. To overcome this problem, the second stage is to adopt genetic programming to establish the error correction model to reduce the prediction error. To evaluating performance of the proposed approach, the carbon dioxide emissions in Taiwan from 2000 to 2015 are forecasted and analyzed. Experimental comparison on various combinations of multiple factors shows that the proposed forecasting approach has higher accuracy than previous approaches.

The article explores the impact of economic growth pressure on carbon emissions based on panel data from China's 277 cities. Moreover, the article analyzes the underlying influence mechanisms as well as regional heterogeneity. The results demonstrate that economic growth pressure significantly increases carbon emissions. Technological innovation and foreign trade constitute the channels through which economic growth pressure affects carbon emissions, but the mediating mechanism of industrial structure upgrading does not exist. Concretely, economic growth pressure increases carbon emissions by reducing technological innovation and foreign trade. In Western China, economic growth pressure has the highest impact on carbon emissions. In central and western China, economic growth pressure has a significant positive effect on carbon emissions. On the contrary, the effect of economic growth pressure on carbon emissions is significantly negative in Eastern China. In Northeast China, the positive effect of economic growth pressure on carbon emissions is statistically insignificant.