Can We Detect Urban‐Scale CO2 Emission Changes Within Medium‐Sized Cities?

The COVID‐19 pandemic resulted in a widespread lockdown during the spring of 2020. Measurements collected on a light rail system in the Salt Lake Valley (SLV), combined with observations from the Utah Urban Carbon Dioxide Network observed a notable decrease in urban CO2 concentrations during the spring of 2020 relative to previous years. These decreases coincided with a ∼30% reduction in average traffic volume. CO2 measurements across the SLV were used within a Bayesian inverse model to spatially allocate anthropogenic emission reductions for the first COVID‐19 lockdown. The inverse model was first used to constrain anthropogenic emissions for the previous year (2019) to provide the best possible estimate of emissions for 2020, before accounting for emission reductions observed during the COVID‐19 lockdown. The posterior emissions for 2019 were then used as the prior emission estimate for the 2020 COVID‐19 lockdown analysis. Results from the inverse analysis suggest that the SLV observed a 20% decrease in afternoon CO2 emissions from March to April 2020 (−90.5 tC hr−1). The largest reductions in CO2 emissions were centered over the northern part of the valley (downtown Salt Lake City), near major roadways, and potentially at industrial point sources. These results demonstrate that CO2 monitoring networks can track reductions in CO2 emissions even in medium‐sized cities like Salt Lake City.Alternate :Plain Language SummaryHigh‐density measurements of CO2 were combined with a statistical model to estimate emission reductions across Salt Lake City during the COVID‐19 lockdown. Reduced traffic throughout the COVID‐19 lockdown was likely the primary driver behind lower CO2 emissions in Salt Lake City. There was also evidence that industrial‐based emission sources may of had an observable decrease in CO2 emissions during the lockdown. Finally, this analysis suggests that high‐density CO2 monitoring networks could be used to track progress toward decarbonization in the future.

Spatiotemporal characteristics and co-effects of air quality and carbon dioxide emissions changes during the COVID-19 epidemic lockdown measures in China.

The COVID-19 pandemic prompted several nations, including China, to enact unprecedented lockdown measures, leading to significant alterations in environmental conditions. Previous studies have solely analysed the impact of lockdown measures on air pollutants or carbon dioxide (CO2) emissions during the COVID-19 pandemic in China, but few have focused on the spatio-temporal change characteristics and synergistic effects between the two. In this study, we constructed a methodological framework to examine the spatiotemporal characteristics and co-effects of air quality (PM2.5, SO2, and NO2) and CO2 changes in 324 prefecture-level cities in China due to the COVID-19 blockade measures from January 24 to April 30, 2020, using the regression discontinuity in time method and co-effect control coordinate system. The results show that a significant improvement in air quality and CO2 emissions during the lockdown period, with notable north‒south heterogeneity. During the major lockdown period (January 24 to February 29), the measures resulted in respective reductions of 5.6%, 16.6%, and 25.1% in the concentrations of SO2, NO2, and CO2 nationwide. The proportions of cities with negative treatment effects on PM2.5, SO2, NO2, and CO2 were 39.20%, 70.99%, 84.6%, and 99.38%, respectively. Provinces where concentrations of CO2 and NO2 declined by over 30% were primarily concentrated in southern areas of the 'Yangtze River Defense Line'. Starting from March, the improvement effect of air quality and CO2 has weakened, and the concentration of air pollutants has rebounded. This study offers crucial insights into the causal effects of lockdown measures on air quality changes, and reveals the synergy between air quality and CO2, thereby providing a reference for devising effective air quality improvement and energy-saving emission reduction strategies.

A Comparative Perspective of the Effects of CO2 and Non-CO2 Greenhouse Gas Emissions on Global Solar, Wind, and Geothermal Energy Investment

Greenhouse gas emissions, including carbon dioxide and non-CO2 gases, are mainly generated by human activities such as the burning of fossil fuels, deforestation, and agriculture. These emissions disrupt the natural balance of the global ecosystem and contribute to climate change. However, by investing in renewable energy, we can help mitigate these problems by reducing greenhouse gas emissions and promoting a more sustainable future. This research utilized a panel data model to explore the impact of carbon dioxide and non-CO2 greenhouse gas emissions on global investments in renewable energy. The study analyzed data from 63 countries over the period from 1990 to 2021. Firstly, the study established a relationship between greenhouse gas emissions and clean energy investments across all countries. The findings indicated that carbon dioxide had a positive effect on clean energy investments, while non-CO2 greenhouse gas emissions had a negative impact on all three types of clean energy investments. However, the impact of flood damage as a representative of climate change on renewable energy investment was uncertain. Secondly, the study employed panel data with random effects to examine the relationship between countries with lower or higher average carbon dioxide emissions and their investments in solar, wind, and geothermal energy. The results revealed that non-CO2 greenhouse gas emissions had a positive impact on investments only in wind power in less polluted countries. On the other hand, flood damage and carbon dioxide emissions were the primary deciding factors for investments in each type of clean energy in more polluted countries.

Energy policy simulation in times of crisis: Revisiting the impact of renewable and non-renewable energy production on environmental quality in Germany

In this paper, we examine for the first time in the literature the implications of energy policy alternatives for Germany considering the aftermath of coronavirus as well as Electricity and Gas energy supply shortages. Whilst several policy options are open to the government, the choice of investment in renewable energy generation versus disinvestment in non-renewable energy such as coal energy generation provides divergent impacts in the long term. We utilize data from British Petroleum and the World Bank Development Indicator database for Germany covering 1981 to 2020 to explore a Carbon function by applying a battery of Autoregressive distributed lag model (ARDL), dynamic ARDL and Kernel-Based Regularized Least squares approaches. The particular policy tested is the pledge by Germany to decrease emissions by ∼100% in 2050, and this was integrated through the estimation of dynamic ARDL estimation. The simulation result shows that a ＋61% shock in renewable energy production decreases carbon emissions unlike coal energy production which increases carbon emissions in the beginning but the carbon emissions decrease thereafter. The findings highlight the inevitability of cutting down on coal production, and recommends energy investment alternatives. Hence, Germany's energy policy should contemplate more thoroughly on these factors. © 2023 The Author(s)

Research on the emission reduction effects of carbon trading mechanism on power industry: plant-level evidence from China

PurposeCarbon trading mechanism has been adopted to foster the green transformation of the economy on a global scale, but its effectiveness for the power industry remains controversial. Given that energy-related greenhouse gas emissions account for most of all anthropogenic emissions, this paper aims to evaluate the effectiveness of this trading mechanism at the plant level to support relevant decision-making and mechanism design.Design/methodology/approachThis paper constructs a novel spatiotemporal data set by matching satellite-based high-resolution (1 × 1 km) CO2 and PM2.5 emission data with accurate geolocation of power plants. It then applies a difference-in-differences model to analyse the impact of carbon trading mechanism on emission reduction for the power industry in China from 2007 to 2016.FindingsResults suggest that the carbon trading mechanism induces 2.7% of CO2 emission reduction and 6.7% of PM2.5 emission reduction in power plants in pilot areas on average. However, the reduction effect is significant only in coal-fired power plants but not in gas-fired power plants. Besides, the reduction effect is significant for power plants operated with different technologies and is more pronounced for those with outdated production technology, indicating the strong potential for green development of backward power plants. The reduction effect is also more intense for power plants without affiliation relationships than those affiliated with particular manufacturers.Originality/valueThis paper identifies the causal relationship between the carbon trading mechanism and emission reduction in the power industry by providing an innovative methodology for identifying plant-level emissions based on high-resolution satellite data, which has been practically absent in previous studies. It serves as a reference for stakeholders involved in detailed policy formulation and execution, including policymakers, power plant managers and green investors.

Application of Time Series Analysis Methods to Assess the impact of COVID-19 on Carbon Dioxide Emission Reduction in Connecticut

The primary goals of this study are to determine if the datasets of positive COVID-19 test cases and CO2 emissions from Connecticut over the span of March 24th, 2020-October 31, 2021 are in any ways correlated. With climate change a prominent issue facing the entire world today, it is important to explore methods of providing records of past patterns of greenhouse gas emissions in order to inform decision making that could reduce future ones. Autoregressive integrated moving average (ARIMA) modeling is also implemented in this paper to provide forecasting based on CO2 emissions in CT starting from 2019. The most significant results from this paper are as follows: the CO2 emission data of transportation sectors including ground transportation, domestics aviation, and international aviation and weekly COVID-19 positive test cases data has a strong relationship during the first 28 weeks of the pandemic with a correlation of -86.34%. The CO2 emissions experienced on average a -22.96% change of pre-pandemic vs during initial quarantine conditions and at most a - 44.48% change when comparing the pre-pandemic mean to the during initial quarantine minimum value. Lastly, the ARIMA model found to have the lowest Akaike information criterion (AIC) was ARIMA (4,0,4). In conclusion, in the event of a collective global pandemic and lockdown conditions, less traveling resulting in a correlated decrease of CO2 emissions. This means that perhaps concentrated efforts on reducing unnecessary travel could help mitigate the levels of carbon dioxide emissions as a more long-term solution to climate change opposed to the pandemic's short-term example. © 2022 IEEE.

Pandemic restrictions in 2020 highlight the significance of non-road NOx sources in central London

Fluxes of nitrogen oxides (NOx=NO+NO2) and carbon dioxide (CO2) were measured using eddy covariance at the British Telecommunications (BT) Tower in central London during the coronavirus pandemic. Comparing fluxes to those measured in 2017 prior to the pandemic restrictions and the introduction of the Ultra-Low Emissions Zone (ULEZ) highlighted a 73 % reduction in NOx emissions between the two periods but only a 20 % reduction in CO2 emissions and a 32 % reduction in traffic load. Use of a footprint model and the London Atmospheric Emissions Inventory (LAEI) identified transport and heat and power generation to be the two dominant sources of NOx and CO2 but with significantly different relative contributions for each species. Application of external constraints on NOx and CO2 emissions allowed the reductions in the different sources to be untangled, identifying that transport NOx emissions had reduced by >73 % since 2017. This was attributed in part to the success of air quality policy in central London but crucially due to the substantial reduction in congestion that resulted from pandemic-reduced mobility. Spatial mapping of the fluxes suggests that central London was dominated by point source heat and power generation emissions during the period of reduced mobility. This will have important implications on future air quality policy for NO2 which, until now, has been primarily focused on the emissions from diesel exhausts.

An emissions inventory using flight information reveals the long-term changes of aviation CO2 emissions in China

The low-carbon development of air transport industry is of great significance for China to achieve the commitment of carbon peak and carbon neutrality goals. In order to improve the basic data of aviation CO2 emissions, this study continuously collected full flight information in China from January 2017 to December 2020, and established a flight information database and an aircraft-engine parameter database. On the basis of IPCC's Tier 3B accounting method, this study established a long-term aviation CO2 emissions inventory of China from 2017 to 2020 by calculating and accumulating CO2 emissions of each flight. And aviation CO2 emissions of various provinces and cities in China were calculated combined with spatial allocation method. The results showed that aviation CO2 emissions in China was 104.1, 120.1, 136.9, and 88.3 Mt in 2017, 2018, 2019, and 2020, respectively, with annual growth rates of 15.4%, 14.0%, and −35.3% in 2018, 2019, and 2020, respectively. Affected by the COVID-19 pandemic, aviation CO2 emissions in all 31 provinces and 93% of cities decreased in 2020 compared with 2019. China is in the stage of rapid development of air transport industry, and aviation fossil energy consumption and CO2 emissions have continued to grow in recent years. © 2022 Elsevier Ltd

A Review of Factors Influencing the Adoption of Electric Vehicles in the World

The road transport sector has a direct effect on fossil energy sources, cost, and consumption. Indeed, it has affected the environmental situation reversely with high carbon dioxide emissions. Due to this negative impact, the transition to electric vehicle (EV) technology must be a mandatory target for governments worldwide. To achieve this objective, many countries have developed various policies to promote EV technology buying or retrofitting. Thanks to the adopted policies, the electric technology market share has been growing. Meanwhile, research studies are involved also in this project by studying the benefit of EV technology low total cost of ownership (TCO) to motivate consumers of its utilization. For that purpose, the present paper aims to review the discussed policies, and methods to boost the diffusion of electric technology as a sustainable and reliable solution to overcome the global energy situation despite the different obstacles, barriers, and the pandemic situation (COVID-19), which has affected the consumer economic and social behavior. © 2022 IEEE.

Non-Renewable and Renewable Energies, and COVID-19 Pandemic: Do They Matter for China’s Environmental Sustainability?

Since the emergence of the COVID-19 pandemic, people all around the globe have seen its effects, including city closures, travel restrictions, and stringent security measures. However, the effects of the COVID-19 pandemic extend beyond people’s everyday lives. It impacts the air, water, soil, and carbon emissions as well. This article examines the effect of energy and the COVID-19 pandemic on China’s carbon dioxide emissions in light of the aforementioned context, using the daily data from 20 January 2020 and ending on 20 April 2022. Using the nonlinear autoregressive distributed lag model for empirical analysis, the findings indicate that COVID-19 pandemic confirmed cases and renewable energy advance environmental sustainability due to their negative effects on carbon dioxide emissions, whereas fossil fuel energy hinders environmental sustainability due to its positive effect on carbon dioxide emissions. Moreover, these results are also supported by the results of the frequency domain causality test and the Markow switching regression. In light of these results, there are several policy implications, such as vaccination, renewable energy utilization, and non-renewable energy alternative policies, which have been proposed in this paper.

An emissions inventory using flight information reveals the long-term changes of aviation CO2 emissions in China

The low-carbon development of air transport industry is of great significance for China to achieve the commitment of carbon peak and carbon neutrality goals. In order to improve the basic data of aviation CO2 emissions, this study continuously collected full flight information in China from January 2017 to December 2020, and established a flight information database and an aircraft-engine parameter database. On the basis of IPCC's Tier 3B accounting method, this study established a long-term aviation CO2 emissions inventory of China from 2017 to 2020 by calculating and accumulating CO2 emissions of each flight. And aviation CO2 emissions of various provinces and cities in China were calculated combined with spatial allocation method. The results showed that aviation CO2 emissions in China was 104.1, 120.1, 136.9, and 88.3 Mt in 2017, 2018, 2019, and 2020, respectively, with annual growth rates of 15.4%, 14.0%, and −35.3% in 2018, 2019, and 2020, respectively. Affected by the COVID-19 pandemic, aviation CO2 emissions in all 31 provinces and 93% of cities decreased in 2020 compared with 2019. China is in the stage of rapid development of air transport industry, and aviation fossil energy consumption and CO2 emissions have continued to grow in recent years.

Possibilities to Reduce Co2 Emissions by Using Electric Motors with High Energy Efficiency

The latest Intergovernmental Panel on Climate Change (IPCC) report said that without immediate and deep emissions reductions across all sectors, limiting global warming to 1.5 degrees C is beyond reach. According to a new IEA (International Energy Agency) analysis, CO2 emissions rose by 6% in 2021 to 36.3 billion tonnes, their highest ever level, as the world economy rebounded strongly from the Covid-19-pandemic crisis. The direct greenhouse gas emissions (COx, CH4, NOx) come mostly from agriculture (crops cultivation) and the livestock sector. Indirect reduction of CO2 emissions in livestock farms and the food and beverage industry can involve using electric motors with high energetic efficiency. Electric motors represent worldwide, around 50% of electricity consumption. A recent study highlights that if the world's 300 million industrial motor-driven systems were replaced with optimized, high-efficiency equipment, global electricity consumption could be reduced by 10%. This paper analyses the International and European Commission Regulations for efficiency and the new Ecodesign measures for electric motors.

Atmospheric CO2 concentration and its influence factors during 2020 COVID-19 pandemic in Nanchang: Journal of Nanjing University of Information Science & Technology

The atmospheric CO2 concentrations are mainly influenced by regional sinks/sources and atmospheric transport processes, thus observations in urban area contain essential information of anthropogenic CO2 emissions. To investigate the effect of COVID-19 on atmospheric CO2 concentration and its anthropogenic emissions, this study chose Nanchang city as the study area and used a priori emission inventory with WRF-STILT (Stochastic Time-Inverted Lagrangian Transport) atmospheric transport model to simulate hourly CO2 concentrations from January 24th to April 30th, 2020. In accordance with the government measures to control COVID-19 epidemic, the whole study period was divided into two periods of Level 1 period (from January 24th to March 11th) and Level 2 period (from March 12th to April 30th). Results indicate the model can well capture hourly variations of CO2 concentration, but it overestimated nighttime concentrations due to the negligence of emission source height. During Level 1 period, the observed and simulated afternoon (12:00-18:00) CO2 mole fractions were 433. 63×10-6 and 438. 22×10-6, respectively,in which the anthropogenic emissions were 21.9% overestimated by simulation compared with observations. While during Level 2 period, the observation and simulation were very close as 432. 06×10-6 and 432. 24 × 10-6. The above comparisons indicate that the CO2 emissions can be represented by a priori CO2 emission inventory in Level 2 period, but was overestimated by 21.9% in Level 1 period, and the discrepancy was mainly due to government measures to control COVID-19 pandemic during this period. Besides, the average biological NEE enhancements were generally lower than 2×10-6, indicating a small contribution compared with anthropogenic emissions. The higher PBLH (Planetary Boundary Layer Height) in Level 2 period also offset the enhancement in CO2 emissions, which was also the main reason for the close observations during two periods. Our findings can provide scientific method supports for greenhouse gas emission inversions at urban scale.

China’s Port Carbon Emission Reduction: A Study of Emission-Driven Factors

Ports offer an effective way to facilitate the global economy. However, massive carbon emission during port operating aggravates the atmospheric pollution in port cities. Capturing characteristics of port carbon emission is vital to reduce GHG (greenhouse gas) in the maritime realm as well as to achieve China’s carbon neutral objective. In this work, an integrated framework is proposed for exploring the driving factors of China ports’ emissions combined with stochastic effects on population, affluence and technology regression (STIRPAT), Global Malmquist-Luenberger (GML) and multiple linear regression (MLR). The port efficiency is estimated for each port and the potential driving factors of carbon emission are explored. The results indicate that port carbon emissions have a strong connection with port throughput, productivity, containerization and intermodal transshipment. It is worth noting that the containerization ratio and port physical facility with fossil-free energy improvement have positively correlated with carbon emissions. However, the specific value of waterborne transshipment shows a complex impact on carbon dioxide emission as the ratio increases. The findings reveal that China port authorities need to improve containerization ratio and develop intermodal transportation;meanwhile, it is responsible for port authorities to update energy use and improve energy efficiency in ways to minimize the proportion of non-green energy consumption in accordance with optimizing port operation management including peak shaving and intelligent management systems under a new horizon of clean energy and automatic equipment.

Will COVID-19 affect China's peak CO2 emissions in 2030? An analysis based on the systems dynamics model of green finance

Achieving the peak of carbon dioxide (CO2) emissions requires a large amount of green and low-carbon investment. Whether the green finance system can efficiently support the capital need for achieving the CO2 emissions target in the context of the COVID-19 epidemic is a matter of concern. This paper constructs a system dynamics model (SD model) to illustrate the quantitative relationship between the green finance system and CO2 emissions and introduce the COVID-19 epidemic as a variable to analyze ten simulation scenarios regarding the carbon emissions commitment realization under different green finance and economic growth status. It is shown that: (1) Regardless of the impact of COVID-19, China can meet its commitment by reaching its CO2 emissions peak in 2030 and realizing a 20% non-fossil energy proportion in 2025;(2) Under the impact of the epidemic, the goal can not be obtained in all energy consumption scenarios when the government expenditure on the environment is low. The target year of reaching CO2 emissions peak becomes 2033, 2037, and 2040. The results indicate that reducing government expenditure on environment protection makes the CO2 emissions peak target less likely to be achieved within a given time frame. We also concluded with important policy implications according to the result of the simulations. Overall, this study makes a reference for other economies and researchers to quantitatively predict the interaction relationship between the green finance system and CO2 emissions in the context of COVID-19, which provides policymakers with insights into a joint power of energy consumption upgrading and green capital guidance.

System dynamics model for changing transportation demand during the pandemic in Japan

To realize a sustainable transportation system, it is necessary to estimate the environmental load caused by transportation. Here transportation demand affects carbon dioxide emissions directly. In general, traffic simulations or scenario-based evaluations have been used to predict transportation demand. However, the COVID-19 pandemic that began in late 2019 has changed transportation demand drastically, and such changes have not been considered in conventional simulation models. Therefore, it is important to quantify the impact of the pandemic on transportation demand and its magnitude. In this study, we developed a model focused on describing the changes in transportation demand caused by the COVID-19 pandemic in Japan. We developed a model using system dynamics because this method is effective in describing socio-technical systems such as transportation demand. Based on related studies, we categorized transportation demand by purpose and modeled it based on the cause-and-effect relationship between the amount of transportation and the prevalence of infectious diseases. To verify the developed model, we compared actual data of 2020 in Japan with the output of the model. We set scenarios with varying parameter values that contribute significantly to changes in transportation demand, such as individual awareness of the pandemic. As a result, the developed model was verified at the behavioral level. This model can be used in developing future transportation systems. © 2022 Elsevier B.V.. All rights reserved.

The Prospects of Clay Minerals from the Baltic States for Industrial-Scale Carbon Capture: A Review

Carbon capture is among the most sustainable strategies to limit carbon dioxide emissions, which account for a large share of human impact on climate change and ecosystem destruction. This growing threat calls for novel solutions to reduce emissions on an industrial level. Carbon capture by amorphous solids is among the most reasonable options as it requires less energy when compared to other techniques and has comparatively lower development and maintenance costs. In this respect, the method of carbon dioxide adsorption by solids can be used in the long-term and on an industrial scale. Furthermore, certain sorbents are reusable, which makes their use for carbon capture economically justified and acquisition of natural resources full and sustainable. Clay minerals, which are a universally available and versatile material, are amidst such sorbents. These materials are capable of interlayer and surface adsorption of carbon dioxide. In addition, their modification allows to improve carbon dioxide adsorption capabilities even more. The aim of the review is to discuss the prospective of the most widely available clay minerals in the Baltic States for large-scale carbon dioxide emission reduction and to suggest suitable approaches for clay modification to improve carbon dioxide adsorption capacity.

Impact of Lockdowns and Winter Temperatures on Natural Gas Consumption in Europe

As the COVID‐19 virus spread over the world, governments restricted mobility to slow transmission. Public health measures had different intensities across European countries but all had significant impact on people's daily lives and economic activities, causing a drop of CO2 emissions of about 10% for the whole year 2020. Here, we analyze changes in natural gas use in the industry and gas distribution to the built environment during the first half of year 2020 with daily gas flows data from pipeline and storage facilities in Europe. We find that reductions of industrial gas use reflect decreases in industrial production across most countries. Surprisingly, natural gas use in the built environment also decreased despite most people being confined at home and cold spells in March 2020. Those reductions that we attribute to the impacts of COVID‐19 remain of comparable magnitude to previous variations induced by cold or warm climate anomalies in the cold season. We conclude that climate variations played a larger role than COVID‐19 induced stay‐home orders in natural gas consumption across Europe.

Estimations of carbon footprint from electricity consumption during covid-19 lockdown and pre-lockdown in butuan city

The electricity consumption for commercial, residential, and industrial sectors is considered the primary cause of increasing carbon dioxide emissions. To calculate the carbon footprint, the researcher used Carbon Footprint Ltd. This study aims to quantify the carbon footprint associated with the consumption of electricity by sectors (residential, commercial, industrial, public buildings, and streetlights) in Butuan City during the pre-lockdown period (January and February), and then compare these with the carbon footprint calculated during the lockdown period (March and April 2020). A GIS-based approach was applied to generate the spatial distribution across the 86 barangays of Butuan City. The study findings that the carbon footprint in the lockdown period is ∼ −17% lower than the mean carbon footprint calculated for the pre-lockdown period. In absolute values, the total estimated carbon footprint during the pre-lockdown and lockdown period was ∼ 10,947 mtCo2e and ∼ 9,138 mtCo2e, respectively. Furthermore, the findings imply that the central and northern areas have the highest impact of savings on average ∼ 130 mtCo2e of greenhouse gas avoided by barangays. This research provides quantitative insight to understand the measured generated in lockdown and pre-lockdown periods. © International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives