Rethinking the Bitcoin-carbon neutrality correlation: evidence from global sectoral CO2 emissions

Transforming China’s economic growth pattern from investment-driven to consumption-driven can significantly change global CO2 emissions. This study is the first to analyse the impacts of changes in China’s saving rates on global CO2 emissions both theoretically and empirically. Here, we show that the increase in the saving rates of Chinese regions has led to increments of global industrial CO2 emissions by 189 million tonnes (Mt) during 2007–2012. A 15-percentage-point decrease in the saving rate of China can lower global CO2 emissions by 186 Mt, or 0.7% of global industrial CO2 emissions. Greener consumption in China can lead to a further 14% reduction in global industrial CO2 emissions. In particular, decreasing the saving rate of Shandong has the most massive potential for global CO2 reductions, while that of Inner Mongolia has adverse effects. Removing economic frictions to allow the production system to fit China’s increased consumption can facilitate global CO2 mitigation.

Abstract. Vehicles are among the most important contributors to global anthropogenic CO2 emissions. However, the lack of fuel-, vehicle-type-, and age-specific information about global on-road CO2 emissions in existing datasets, which are available only at the sector level, makes these datasets insufficient for supporting the establishment of emission mitigation strategies. Thus, a fleet turnover model is developed in this study, and CO2 emissions from global on-road vehicles from 1970 to 2020 are estimated for each country. Here, we analyze the evolution of the global vehicle stock over 50 years, identify the dominant emission contributors by vehicle and fuel type, and further characterize the age distribution of on-road CO2 emissions. We find that trucks accounted for less than 5 % of global vehicle ownership but represented more than 20 % of on-road CO2 emissions in 2020. The contribution of diesel vehicles to global on-road CO2 emissions doubled during the 1970–2020 period, driven by the shift in the fuel-type distribution of vehicle ownership. The proportion of CO2 emissions from vehicles in developing countries such as China and India in terms of global emissions from newly registered vehicles significantly increased after 2000, but global CO2 emissions from vehicles that had survived more than 15 years in 2020 still originated mainly from developed countries such as the United States and countries in the European Union. The data are publicly available at https://doi.org/10.6084/m9.figshare.24548008 (Yan et al., 2024).

The impact of the rise of emerging economies on global industrial CO2 emissions: Evidence from emerging economies in Regional Comprehensive Economic Partnership

Adequate CO2 is essential for vegetation, but industrial chimneys and land, space and oceanic vehicles exert tons of excessive CO2 and are mostly responsible for the greenhouse effect, global warming and climate change. Due to COVID-19, CO2 emission was in 2020 at its lowest level compared to prior decades. However, it is unknown how long it will take to reduce CO2 emission to a tolerable point. Furthermore, it is also unknown to what extent it can increase or change in the future. Accurate forecasting of CO2 emissions has real significance for choosing the optimum ways of reducing CO2 emissions. Although some existing models have noticeable CO2 emission forecasting accuracy, the models implemented in this work have more efficacy in prediction due to incorporating COVID-19’s effect on CO2 emission. This paper implements four prediction models using SARIMA (SARIMAX) based on ARIMA. The four models are based on the time period of the surge of the COVID-19 pandemic. The main objective of this work is to compare these four models to suggest an effective model to predict the total CO2 emissions for the future. The study forecasts global total CO2 emission from 2022 to 2027 for near future prediction, 2022 to 2054 for future prediction and 2022 to 2072 for far future prediction. Among the various error measures, mean absolute percentage error (MAPE) is chosen for accuracy comparison. The calculation yields different accuracy for the four SARIMAX models. The MAPEs for the four methods are: pre-COV (MAPE: 0.32), start-COV (MAPE: 0.28), trans-COV (MAPE: 0.19), post-COV (MAPE: 0.09). The MAPE value is relatively low for post-COV (MAPE: 0.09). Hence, it can be inferred that post-COV are suitable models to forecast the global total CO2 emission for the future. The post-COV predictions for the global total CO2 emission for the years 2022 to 2027 are: 36,218.59, 36,733.69, 37,238.29, 37,260.88, 37,674.01 and 37,921.47 million tons (MT). This study successfully predicts CO2 emission either for the COVID-19 period or the post-COVID-19 normal periods. The Machine Learning (ML) method used in this study has shown good agreement with the IPCC model in predicting the past emissions, the current emissions due to COVID-19 and the emissions of the upcoming future. These prediction results can be an asset for the decision support system to develop a suitable policy for global CO2 emission reduction. For future research, a number of other external influence variables responsible for CO2 emission can be added for finer forecasts. This research is an original work in predicting COVID-19-affected CO2 emission using AI through the ML methodology.

Considering investment resources when assessing potential CO2 reductions of CHP – a case study

Study on the gravity movement and decoupling state of global energy-related CO2 emissions.

The global CO2 emission cost of geographic shifts in international sourcing

A global inventory of carbon monoxide emissions from motor vehicles

This study analyzes CO2 emissions reduction targets for various countries and geopolitical regions by the year 2030 in order to stabilize atmospheric concentrations of CO2 at the level of 450 ppm (550 ppm including non CO2 greenhouse gases). It also determines CO2 intensity cuts that would be needed in those countries and regions if the emission reductions were achieved through intensity-based targets while assuming no effect on forecasted economic growth. Considering that the stabilization of CO2 concentrations at 450 ppm requires the global trend of CO2 emissions to reverse before 2030, this study develops two scenarios: reversing the global CO2 trend in (i) 2020 and (ii) 2025. The study shows that global CO2 emissions would be 42 percent above the 1990 level in 2030 if the increasing trend of global CO2 emissions is reversed by 2020. If reversing the trend is delayed by 5 years, the 2030 global CO2 emissions would be 52 percent higher than the 1990 level. The study also finds that to achieve these targets while maintaining assumed economic growth, the global average CO2 intensity would require a 68 percent drop from the 1990 level or a 60 percent drop from the 2004 level by 2030.

Atmospheric stabilization of CO2 emissions: Near-term reductions and absolute versus intensity-based targets

Timely, fine-grained gridded carbon emission datasets are particularly important for global climate change research. Often, fine-grained datasets are challenging to visualize over the globe, and clear visualization tools are also needed. Therefore, we present a near-real-time global gridded daily CO2 emissions dataset (GRACED). GRACED provides gridded CO2 emissions at a 0.1° × 0.1° spatial resolution and 1-day temporal resolution from cement production and fossil fuel combustion over seven sectors, including power, industry, residential consumption, ground transportation, domestic aviation, international aviation, and international shipping. GRACED is prepared from the near-real-time daily national CO2 emissions estimates (Carbon Monitor), multi-source spatial activity data and satellite NO2 data for time variations of those spatial activity data. Here, we examined the spatial patterns of sectoral CO2 emission changes from January 1, 2019, to December 31, 2021. In 2021, most regions showed rapid rebounds in carbon emissions compared with 2020, reflecting the continuing challenges to accelerate climate mitigation in the post-COVID era. GRACED provides the most timely and more refined overview than any other previously published datasets, which enables more accurate and timely identification of when and where fossil CO2 emissions have rebounded and decreased as the world recovers from COVID-19 and witnesses contrasted efforts to decarbonize energy systems. Uncertainty analysis of GRACED gives a grid-level two-sigma uncertainty of value of ±19.9%, indicating the reliability of GRACED was not sacrificed for the sake of higher spatiotemporal resolution that GRACED provides. In addition, we also examined the distribution of emission in a grid-wise perspective for major emission datasets, and compared it with GRACED. The similarity in emission distribution was observed in GRACED and other datasets. One of the advantages of our dataset is that it provides worldwide near-real-time monitoring of CO2 emissions with different fine spatial scales at the sub-national level, such as cities, thus enhancing our comprehension of spatial and temporal changes in CO2 emissions and anthropogenic activities. With the continued extension of GRACED time series, we present crucial daily-level input to analyze CO2 emission changes in the post-COVID era, which will ultimately facilitate and aid in designing more localized and adaptive management policies for the purpose of climate change mitigation in the post-COVID era.

Global efforts are needed to reduce CO2 emissions and guarantee a safe climate system that supports global sustainable development and wellbeing. Understanding drivers of global CO2 emissions is of great importance as the world strives to achieve global climate mitigation goals. Using structural decomposition analysis (SDA) we identify the key drivers behind changes in global and regional CO2 emissions from 2000 to 2014. We find that growth in global CO2 (+10.8 GtCO2) emissions was driven by increasing affluence (+14.3GtCO2) which outpaced the downward influence of changes in technology (−9.2GtCO2). Global results, however, mask considerable regional heterogeneity and different dynamics at the country level. The affluence effect was predominantly driven by capital investments in developing and emerging economies. In high income regions, technological improvements were strong enough to offset the positive pressures from increasing affluence. In these countries changes in population and trade structure were more important drivers than affluence. Although some countries/regions (e.g. EUR) demonstrate continuous and consistent emissions reductions these efforts need to increase considerably to reach climate goals.

It is believed that the global CO2 emissions have to begin dropping in the near future to limit the temperature increase within 2 degrees by 2100. So it is of great concern to environmentalists and national decision-makers to know how the global or national CO2 emissions would trend. This paper presented an approach to project the future CO2 emissions from the perspective of optimal economic growth, and applied this model to the cases of China and the United States, whose CO2 emissions together contributed to more than 40% of the global emissions. The projection results under the balanced and optimal economic growth path reveal that the CO2 emissions will peak in 2029 for China and 2024 for the USA owing to their empirically implied pace of energy efficiency improvement. Moreover, some abatement options are analyzed for China, which indicate that 1) putting up the energy price will decrease the emissions at a high cost; 2) enhancing the decline rate of energy intensity can significantly mitigate the emissions with a modest cost; and 3) the energy substitution policy of replacing carbon intensive energies with clean ones has considerable potential to alleviate emissions without compromising the economic development.

Precise and high-resolution carbon dioxide (CO2) emission data is of great importance in achieving carbon neutrality around the world. Here we present for the first time the near-real-time Global Gridded Daily CO2 Emissions Dataset (GRACED) from fossil fuel and cement production with a global spatial resolution of 0.1° by 0.1° and a temporal resolution of 1 day. Gridded fossil emissions are computed for different sectors based on the daily national CO2 emissions from near-real-time dataset (Carbon Monitor), the spatial patterns of point source emission dataset Global Energy Infrastructure Emissions Database (GID), Emission Database for Global Atmospheric Research (EDGAR), and spatiotemporal patters of satellite nitrogen dioxide (NO2) retrievals. Our study on the global CO2 emissions responds to the growing and urgent need for high-quality, fine-grained, near-real-time CO2 emissions estimates to support global emissions monitoring across various spatial scales. We show the spatial patterns of emission changes for power, industry, residential consumption, ground transportation, domestic and international aviation, and international shipping sectors from January 1, 2019, to December 31, 2020. This gives thorough insights into the relative contributions from each sector. Furthermore, it provides the most up-to-date and fine-grained overview of where and when fossil CO2 emissions have decreased and rebounded in response to emergencies (e.g., coronavirus disease 2019 [COVID-19]) and other disturbances of human activities of any previously published dataset. As the world recovers from the pandemic and decarbonizes its energy systems, regular updates of this dataset will enable policymakers to more closely monitor the effectiveness of climate and energy policies and quickly adapt.

Economic globalization and concomitant growth in international trade since the late 1990s have profoundly reorganized global production activities and related CO2 emissions. Here we show trade among developing nations (i.e., South–South trade) has more than doubled between 2004 and 2011, which reflects a new phase of globalization. Some production activities are relocating from China and India to other developing countries, particularly raw materials and intermediate goods production in energy-intensive sectors. In turn, the growth of CO2 emissions embodied in Chinese exports has slowed or reversed, while the emissions embodied in exports from less-developed regions such as Vietnam and Bangladesh have surged. Although China’s emissions may be peaking, ever more complex supply chains are distributing energy-intensive industries and their CO2 emissions throughout the global South. This trend may seriously undermine international efforts to reduce global emissions that increasingly rely on rallying voluntary contributions of more, smaller, and less-developed nations.