Evaluating the Mutual Relationship between IPAT/Kaya Identity Index and ODIAC-Based GOSAT Fossil-Fuel CO2 Flux: Potential and Constraints in Utilizing Decomposed Variables

Migration and Transformation of Taxane Allelochemicals in Soil

The migration and transformation of allelochemicals are important topics in the exploration of allelopathy. Current research on the migration of allelochemicals mostly uses soil column and thin layer methods and verifies it by sowing plant seeds. However, traditional methods inevitably ignore the flux caused by the movement of allelochemicals carried by water. In fact, the flux determines the amount of allelochemicals that directly affect plants. In this work, a method of microdialysis combined with a soil column and UPLC-MS/MS to detect the flux of allelochemicals was developed for the first time and successfully applied to the detection of five taxane allelochemicals in soil. Meanwhile, by adding taxane allelochemicals to the soil and detecting their transformation products using UPLC-MS/MS, the half-life of taxane in the soil was determined, and the transformation pathway of taxane allelochemicals in the soil was further speculated.

Carbon emissions predicting and decoupling analysis based on the PSO-ELM combined prediction model: evidence from Chongqing Municipality, China

The "14th Five-Year Plan" period is a crucial phase for China to achieve the goal of carbon peaking and carbon neutrality (referred to as the "double carbon"). Thus, it is very important to analyze the main factors affecting carbon emissions and accurately predict the change of carbon emissions to achieve the goal of double carbon. For the slow data updates and the low accuracy of traditional prediction models about the carbon emissions, the key factors of carbon emissions change selected by gray correlation method and the consumption of coal, oil, and natural gas were input into four single prediction models: gray prediction model GM(1,1), ridge regression, BP neural network, and WOA-BP neural network to obtain the fitted and predicted values of carbon emissions, which serve as input to the particle swarm optimization-extreme learning machine (PSO-ELM) model together. Based on the PSO-ELM combined prediction method above and the scenario prediction indicators constructed according to relevant policy documents of Chongqing Municipality, the carbon emission values of Chongqing Municipality during the 14th Five-Year Plan period are predicted in this paper. The empirical results show that carbon emissions of Chongqing Municipality still maintain an upward trend, but the growth rate slow down compared with 1998 to 2018. In general, the carbon emission and GDP of Chongqing Municipality showed a weak decoupling state during 1998 to 2025. By calculation, the PSO-ELM combined prediction model is superior to the above four single prediction models in carbon emission prediction and has good property by the robust testing. The research results can enrich the combined prediction method about the carbon emissions and provide policy suggestions for Chongqing's low-carbon development during the 14th Five-Year Plan period.

City-level emission peak and drivers in China

China is playing an increasing role in global climate change mitigation, and local authorities need more city-specific information on the emissions trends and patterns when designing low-carbon policies. This study provides the most comprehensive CO₂ emission inventories of 287 Chinese cities from 2001 to 2019. The emission inventories are compiled for 47 economic sectors and include energy-related emissions for 17 types of fossil fuels and process-related emissions from cement production. We further investigate the state of the emission peak in each city and reveal hidden driving forces. The results show that 38 cities have proactively peaked their emissions for at least five years and another 21 cities also have emission decline, but passively. The 38 proactively peaked cities achieved emission decline mainly by efficiency improvements and structural changes in energy use, while the 21 passively emission declined cities reduced emissions at the cost of economic recession or population loss. We propose that those passively emission declined cities need to face up to the reasons that caused the emission to decline, and fully exploit the opportunities provided by industrial innovation and green investment brought by low-carbon targets to achieve economic recovery and carbon mitigation goals. Proactively peaked cities need to seek strategies to maintain the downward trend in emissions and avoid an emission rebound and thus provide successful models for cities with still growing emissions to achieve an emission peak.

Estimating CO2 emissions from emergency-supply transport: The case of COVID-19 vaccine global air transport

The environmental cost of disaster-related emergency supplies is significant. However, little research has been conducted on the estimation of emergency-supply transportation-related carbon emissions. This study created an "emergency supply emission estimation methodology" (ESEEM). The CO₂ emissions from the global air dispatch of COVID-19 vaccines were estimated using two hypothetical scenarios of one dose per capita and additional doses secured. The robustness of the model was tested with the Monte Carlo Simulation method (MCM) based one-sample t-test. The model was validated using the "Expression of Uncertainty in Measurement (GUM)" and GUM's MCM approaches. The results showed that to dispatch at least one dose of the COVID-19 vaccine to 7.8 billion people, nearly 8000 Boeing 747 flights will be needed, releasing approximately 8.1 ± 0.30 metric kilotons (kt) of CO₂. As countries secure additional doses, these figures will increase to 14,912 flights and about 15 ± 0.48 kt of CO₂. According to the variance-based sensitivity analysis, the total number of doses (population), technology, and wealth play a significant role in determining CO₂ emissions across nations. Thus, wealthy nations' long-term population reduction efforts, technological advancements, and mitigation efforts can benefit the environment as a whole and the CO₂ burdens associated with current COVID-19 and any future disasters' emergency-supply transportation.

Comparative Evaluation for Tracking the Capability of Solar Cell Malfunction Caused by Soil Debris between UAV Video versus Photo-Mosaic

Monitoring the malfunction of the solar cells (for instance, 156 mm by 156 mm) caused by the soil debris requires a very low flight altitude when taking aerial photos, utilizing the autopilot function of unmanned aerial vehicle (UAV). The autopilot flight can only operate at a certain level of altitude that can guarantee collision avoidance for flight obstacles (for instance, power lines, trees, buildings) adjacent to the place where the solar panel is installed. For this reason, aerial photos taken by autopilot flight capture unnecessary objects (surrounding buildings and roads) around the solar panel at a tremendous level. Therefore, the autopilot-based thermal imaging causes severe data redundancy with very few matched key-points around the malfunctioned solar cells. This study aims to explore the tracking capability on soil debris defects in solar cell scale between UAV video versus photo-mosaic. This study experimentally validated that the video-based thermal imaging can track the thermal deficiency caused by the malfunction of the solar cell at the level of the photo-mosaic in terms of correlation of thermal signatures (0.98–0.99), detection on spatial patterns (81–100%), and distributional property (90–95%) with 2.5–3.4 times more matched key-points on solar cells. The results of this study could serve as a valuable reference for employing video stream in the process of investigating soil debris defects in solar cell scale.

Did Polycentric and Compact Structure Reduce Carbon Emissions? A Spatial Panel Data Analysis of 286 Chinese Cities from 2002 to 2019

Curbing carbon emissions by restricting economic growth could decrease human well-being across the world and especially in developing countries, suggesting that we need to find alternative approaches to reducing carbon emissions. Against this background, this paper investigates the relationship between urban spatial structure and carbon emissions in the Chinese context from 2002 to 2019. Specifically, urban spatial structure of 286 Chinese cities, represented by the two dimensions of polycentricity and compactness, are calculated based on the gridded (1 km × 1 km) LandScan dataset on population, while carbon emissions of these cities are aggregated from the gridded (1 km × 1 km) Open-source Data Inventory for Anthropogenic CO2 (ODIAC) dataset on carbon emissions. The empirical results based on different regression models find that overall (1) more dispersed and less monocentric (i.e., less compact and more polycentric) cities are often associated with lower levels of carbon emissions, ceteris paribus; (2) the impact of polycentricity on carbon emissions could be moderated by the economic development levels of Chinese cities. For cities with gross domestic product of more than 173 billion yuan, a more polycentric spatial structure is usually associated with a higher level of carbon emissions; (3) a city’s urban spatial structure could have positive spatial spillovers on carbon emissions of its neighboring cities.

Influencing factors and decoupling analysis of carbon emissions in China's manufacturing industry

The manufacturing industry directly reflects national productivity, and it is also an industry with high energy consumption and severe carbon emissions. This study decomposes the influential factors on carbon emissions in China's manufacturing industry from 1995 to 2018 into industry value added, energy consumption, fixed asset investment, carbon productivity, energy structure, energy intensity, investment carbon intensity, and investment efficiency by Generalized Divisia Index Model. The decoupling analysis of carbon emissions and industry value added is carried out to investigate the states of the manufacturing industry under the pressure of "low carbon" and "economy." Results show that first, fixed asset investment is the driving force of carbon emissions, followed by industry value added; investment carbon intensity, carbon productivity, investment efficiency, and energy intensity are the mitigating factors; simultaneously, the impacts of energy consumption and energy structure are fluctuating. Second, the decoupling of manufacturing has improved, especially in the light industry. Third, the decoupling of carbon emissions and economic development is mainly dominated by the decoupling of energy consumption and industry added value. Therefore, reducing the proportion of coal consumption and optimizing the energy structure are significant ways to promote the low-carbon development of the manufacturing industry.

Evaluating the Correlation between Thermal Signatures of UAV Video Stream versus Photomosaic for Urban Rooftop Solar Panels

The unmanned aerial vehicle (UAV) autopilot flight to survey urban rooftop solar panels needs a certain flight altitude at a level that can avoid obstacles such as high-rise buildings, street trees, telegraph poles, etc. For this reason, the autopilot-based thermal imaging has severe data redundancy—namely, that non-solar panel area occupies more than 99% of ground target, causing a serious lack of the thermal markers on solar panels. This study aims to explore the correlations between the thermal signatures of urban rooftop solar panels obtained from a UAV video stream and autopilot-based photomosaic. The thermal signatures of video imaging are strongly correlated (0.89–0.99) to those of autopilot-based photomosaics. Furthermore, the differences in the thermal signatures of solar panels between the video and photomosaic are aligned in the range of noise equivalent differential temperature with a 95% confidence level. The results of this study could serve as a valuable reference for employing video stream-based thermal imaging to urban rooftop solar panels.

No evidence for global decrease in CO2 concentration during the first wave of COVID-19 pandemic

Numerous studies have reported that CO₂ emissions have decreased because of global lockdown during the first wave of the COVID-19 pandemic. However, previous estimates of the global CO₂ concentration before and after the outbreak of the COVID-19 pandemic are limited because they are based on energy consumption statistics or local specific in-situ observations. The aim of the study was to explore objective evidence for various previous studies that have claimed the global CO₂ concentration decreased during the first wave of the COVID-19 pandemic. There are two ways to measure the global CO₂ concentration: from the top-down using satellites and the bottom-up using ground stations. We implemented the time-series analysis by comparing the before and after the inflection point (first wave of COVID-19) with the long-term CO₂ concentration data obtained from World Meteorological Organization Global Atmosphere Watch (WMO GAW) and Greenhouse Gases Observing Satellite (GOSAT). Measurements from the GOSAT and GAW global monitoring stations show that the CO₂ concentrations in Europe, China, and the USA have continuously risen in March and April 2020 compared with the same months in 2019. These data confirm that the global lockdown during the first wave of the COVID-19 pandemic did not change the vertical CO₂ profile at the global level from the ground surface to the upper layer of the atmosphere. The results of this study provide an important foundation for the international community to explore policy directions to mitigate climate change in the upcoming post-COVID-19 period.

Comparative Evaluation of Top-Down GOSAT XCO2 vs. Bottom-Up National Reports in the European Countries

Submitting national inventory reports (NIRs) on emissions of greenhouse gases (GHGs) is obligatory for parties of the United Nations Framework Convention on Climate Change (UNFCCC). The NIR forms the basis for monitoring individual countries’ progress on mitigating climate change. Countries prepare NIRs using the default bottom–up methodology of the Intergovernmental Panel on Climate Change (IPCC), as approved by the Kyoto protocol. We provide tangible evidence of the discrepancy between official bottom–up NIR reporting (unit: tons) versus top–down XCO2 reporting (unit: ppm) within the European continent, as measured by the Greenhouse Gases Observing Satellite (GOSAT). Bottom–up NIR (annual growth rate of CO2 emission from 2010 to 2016: −1.55%) does not show meaningful correlation (geographically weighted regression coefficient = −0.001, R2 = 0.024) to top–down GOSAT XCO2 (annual growth rate: 0.59%) in the European countries. The top five countries within the European continent on carbon emissions in NIR do not match the top five countries on GOSAT XCO2 concentrations. NIR exhibits anthropogenic carbon-generating activity within country boundaries, whereas satellite signals reveal the trans-boundary movement of natural and anthropogenic carbon. Although bottom–up NIR reporting has already gained worldwide recognition as a method to track national follow-up for treaty obligations, the single approach based on bottom–up did not present background atmospheric CO2 density derived from the air mass movement between the countries. In conclusion, we suggest an integrated measuring, reporting, and verification (MRV) approach using top–down observation in combination with bottom–up NIR that can provide sufficient countrywide objective evidence for national follow-up activities.

Evaluating the Causal Relations between the Kaya Identity Index and ODIAC-Based Fossil Fuel CO2 Flux

The Kaya identity is a powerful index displaying the influence of individual carbon dioxide (CO2) sources on CO2 emissions. The sources are disaggregated into representative factors such as population, gross domestic product (GDP) per capita, energy intensity of the GDP, and carbon footprint of energy. However, the Kaya identity has limitations as it is merely an accounting equation and does not allow for an examination of the hidden causalities among the factors. Analyzing the causal relationships between the individual Kaya identity factors and their respective subcomponents is necessary to identify the real and relevant drivers of CO2 emissions. In this study we evaluated these causal relationships by conducting a parallel multiple mediation analysis, whereby we used the fossil fuel CO2 flux based on the Open-Source Data Inventory of Anthropogenic CO2 emissions (ODIAC). We found out that the indirect effects from the decomposed variables on the CO2 flux are significant. However, the Kaya identity factors show neither strong nor even significant mediating effects. This demonstrates that the influence individual Kaya identity factors have on CO2 directly emitted to the atmosphere is not primarily due to changes in their input factors, namely the decomposed variables.