Sphagnum mosses, the impact of disturbances and anthropogenic management actions on their ecological role in CO2 fluxes generated in peatland ecosystems.

Mosses of the genus Sphagnum are the dominant vegetation in most pristine peatlands in temperate and high-latitude regions. They play a crucial role in carbon sequestration, being responsible for ca. 50% of carbon accumulation through their active participation in peat formation. They have a significant influence on the dynamics of CO2 emissions due to an efficient maximum potential photosynthetic rate, lower respiration rates, and the production of a recalcitrant litter whose decomposition is gradual. However, various anthropogenic disturbances and land use management actions that favor its reestablishment have the potential to modify the dynamics of these CO2 emissions. Therefore, the objective of this review is to discuss the role of Sphagnum in CO2 emissions generated in peatland ecosystems, and to understand the impacts of anthropogenic practices favorable and detrimental to Sphagnum on these emissions. Based on our review, increased Sphagnum cover reduces CO2 emissions and fosters C sequestration, but drainage transforms peatlands dominated by Sphagnum into a persistent source of CO2 due to lower gross primary productivity of the moss and increased respiration rates. Sites with moss removal used as donor material for peatland restoration emit twice as much CO2 as adjacent undisturbed natural sites, and those with commercial Sphagnum extraction generate almost neutral CO2 emissions, yet both can recover their sink status in the short term. The reintroduction of fragments and natural recolonization of Sphagnum in transitional peatlands, can reduce emissions, recover, or increase the CO2 sink function in the short and medium term. Furthermore, Sphagnum paludiculture is seen as a sustainable alternative for the use of transitional peatlands, allowing moss production strips to become CO2 sink, however, it is necessary to quantify the emissions of all the components of the field of production (ditches, causeway), and the biomass harvested from the moss to establish a final closing balance of C.

Province-Level Decarbonization Potentials for China's Road Transportation Sector.

Decarbonizing road transportation is an important task in achieving China's climate goals. Illustrating the mitigation potentials of announced policies and identifying additional strategies for various vehicle fleets are fundamental in optimizing future control pathways. Herein, we developed a comprehensive analysis of carbon dioxide (CO2) emissions from on-road vehicles as well as their mitigation potentials based on real-world databases and up-to-date policy scenarios. Total CO2 emissions of China's road transportation are estimated to be 1102 million tons (Mt) in 2022 and will continue to increase if future strategies are implemented as usual. Under current development trend and announced policy controls (i.e., integrated scenario), annual CO2 emissions are estimated to peak at 1235 Mt in 2025 and then decline to approximately 200 Mt around 2050. The scenario analysis indicates that electrification of passenger vehicles emerges as the most imperative decarbonization strategy for achieving carbon peak before 2030. Additionally, fuel economy improvement of conventional vehicles is identified to be effective for CO2 emission reduction for trucks until 2035 while new energy vehicle promotion shows great mitigation potentials in the long term. This study provides insight into heterogeneous low-carbon transportation transition strategies and valuable support for achieving China's dual-carbon goals.

China's Fossil Fuel CO2 Emissions Estimated Using Surface Observations of Coemitted NO2.

Accurate estimates of fossil fuel CO2 (FFCO2) emissions are of great importance for climate prediction and mitigation regulations but remain a significant challenge for accounting methods relying on economic statistics and emission factors. In this study, we employed a regional data assimilation framework to assimilate in situ NO2 observations, allowing us to combine observation-constrained NOx emissions coemitted with FFCO2 and grid-specific CO2-to-NOx emission ratios to infer the daily FFCO2 emissions over China. The estimated national total for 2016 was 11.4 PgCO2·yr-1, with an uncertainty (1σ) of 1.5 PgCO2·yr-1 that accounted for errors associated with atmospheric transport, inversion framework parameters, and CO2-to-NOx emission ratios. Our findings indicated that widely used "bottom-up" emission inventories generally ignore numerous activity level statistics of FFCO2 related to energy industries and power plants in western China, whereas the inventories are significantly overestimated in developed regions and key urban areas owing to exaggerated emission factors and inexact spatial disaggregation. The optimized FFCO2 estimate exhibited more distinct seasonality with a significant increase in emissions in winter. These findings advance our understanding of the spatiotemporal regime of FFCO2 emissions in China.

Enhancing Space-Based Tracking of Fossil Fuel CO2 Emissions via Synergistic Integration of OCO-2, OCO-3, and TROPOMI Measurements.

Top-down estimates of fossil fuel CO2 (FFCO2) emissions are crucial for tracking emissions and evaluating mitigation strategies. However, their practical application is hindered by limited data coverage and overreliance on NOx-to-CO2 emission ratios from emission inventories. We developed the Machine Learning-Driven Mapping Satellite-based XCO2en (ML-MSXE) model using the column-averaged dry-air mole fraction of CO2 enhancement (XCO2en) derived from OCO-2 and OCO-3 measurements to reconstruct the XCO2en distribution for monitoring FFCO2 emissions. Compared to the previous Machine Learning-Driven Deriving XCO2en from Mapped XCO2 (ML-DXEMX) model, ML-MSXE enhances the utilization of TROPOMI NO2 measurements, increasing their relative contribution from 4.3 to 21.7%, thereby improving XCO2en reconstruction accuracy and enhancing the ability to track emissions. Despite the COVID-19 lockdown, XCO2en levels in China rose from 1.33 ± 1.06 in 2019 to 1.39 ± 1.01 ppm in 2021. In February 2020, while the national average rate of XCO2en decline (16.3%) aligned with the reduction in FFCO2 emissions estimated by inventories, XCO2en further revealed varying rates of decline between cities. Furthermore, the spatial distribution of XCO2en identified hotspots where FFCO2 emissions might be underestimated by inventories. This study presents a space-based approach for monitoring FFCO2 emissions, offering valuable insights for assessing carbon neutrality progress and informing policy.

Characterizing and Predicting nvPM Size Distributions for Aviation Emission Inventories and Environmental Impact.

Concerns about civil aviation's air quality and environmental impacts have led to recent regulations on nonvolatile particulate matter (nvPM) mass and number emissions. Although these regulations do not mandate measuring particle size distribution (PSD), understanding PSDs is vital for assessing the environmental impacts of aviation nvPM. This study introduces a comprehensive data set detailing PSD characteristics of 42 engines across 19 turbofan types, ranging from unregulated small business jets to regulated large commercial aircraft. Emission tests were independently performed by using the European and Swiss reference nvPM sampling and measurement systems with parallel PSD measurements. The geometric mean diameter (GMD) at the engine exit strongly correlated with the nvPM number-to-mass ratio (N/M) and thrust, varying from 7 to 52 nm. The engine-exit geometric standard deviation ranged from 1.7 to 2.5 (mean of 2.05). The study proposes empirical correlations to predict GMD from N/M data of emissions-certified engines. These predictions are expected to be effective for conventional rich-burn engines and might be extended to novel combustor technologies if additional data become available. The findings support the refinement of emission models and help in assessing the aviation non-CO2 climate and air quality impacts.

Impacts of Spatial Resolution and XCO2 Precision on Satellite Capability for CO2 Plumes Detection.

Greenhouse gas satellites can provide consistently global CO2 data which are important inputs for the top-down inverse estimation of CO2 emissions and their dynamic changes. By tracking greenhouse gas emissions, policymakers and businesses can identify areas where reductions are needed most and implement effective strategies to reduce their impact on the environment. Monitoring greenhouse gases provides valuable data for scientists studying climate change. The requirements for CO2 emissions monitoring and verification support capacity drive the payload design of future CO2 satellites. In this study, we quantitatively evaluate the performance of satellite in detecting CO2 plumes from power plants based on an improved Gaussian plume model, with focus on impacts of the satellite spatial resolution and the satellite-derived XCO2 precision under different meteorological conditions. The simulations of CO2 plumes indicate that the enhanced spatial resolution and XCO2 precision can significantly improve the detection capability of satellite, especially for small-sized power plants with emissions below 6 Mt CO2/yr. The satellite-detected maximum of XCO2 enhancement strongly varies with the wind condition. For a satellite with a XCO2 precision of 0.7 ppm and a spatial resolution of 2 km, it can recognize a power plant with emissions of 2.69 Mt CO2/yr at a wind speed of 2 m/s, while its emission needs be larger than 5.1 Mt CO2/yr if the power plant is expected to be detected at a wind speed of 4 m/s. Considering the uncertainties in the simulated wind field, the satellite-derived XCO2 measurements and the hypothesized CO2 emissions, their cumulative contribution to the overall accuracy of the satellite's ability to identify realistic enhancement in XCO2 are investigated in the future. The uncertainties of ΔXCO2 caused by the uncertainty in wind speed is more significant than those introduced from the uncertainty in wind direction. In the case of a power plant emitting 5.1 Mt CO2/yr, with the wind speed increasing from 0.5 m/s to 4 m/s, the simulated ΔXCO2 uncertainty associated with the wind field ranges from 3.75 ± 2.01 ppm to 0.46 ± 0.24 ppm and from 1.82 ± 0.95 ppm to 0.22 ± 0.11 ppm for 1 × 1 km2 and 2 × 2 km2 pixel size, respectively. Generally, even for a wind direction with a higher overall uncertainty, satellite still has a more effective capability for detecting CO2 emission on this wind direction, because there is more rapid growth for simulated maximal XCO2 enhancements than that for overall uncertainties. A designed spatial resolution of satellite better than 1 km and a XCO2 precision higher than 0.7 ppm are suggested, because the CO2 emission from small-sized power plants is much more likely be detected when the wind speed is below 3 m/s. Although spatial resolution and observed precision parameters are not sufficient to support the full design of future CO2 satellites, this study still can provide valuable insights for enhancing satellite monitoring of anthropogenic CO2 emissions.

Impact of Land Use and Land Cover (LULC) Changes on Carbon Stocks and Economic Implications in Calabria Using Google Earth Engine (GEE).

Terrestrial ecosystems play a crucial role in global carbon cycling by sequestering carbon from the atmosphere and storing it primarily in living biomass and soil. Monitoring terrestrial carbon stocks is essential for understanding the impacts of changes in land use on carbon sequestration. This study investigates the potential of remote sensing techniques and the Google Earth Engine to map and monitor changes in the forests of Calabria (Italy) over the past two decades. Using satellite-sourced Corine land cover datasets and the InVEST model, changes in Land Use Land Cover (LULC), and carbon concentrations are analyzed, providing insights into the carbon dynamics of the region. Furthermore, cellular automata and Markov chain techniques are used to simulate the future spatial and temporal dynamics of LULC. The results reveal notable fluctuations in LULC; specifically, settlement and bare land have expanded at the expense of forested and grassland areas. These land use and land cover changes significantly declined the overall carbon stocks in Calabria between 2000 and 2024, resulting in notable economic impacts. The region experienced periods of both decline and growth in carbon concentration, with overall losses resulting in economic impacts up to EUR 357.57 million and carbon losses equivalent to 6,558,069.68 Mg of CO 2 emissions during periods of decline. Conversely, during periods of carbon gain, the economic benefit reached EUR 41.26 million, with sequestered carbon equivalent to 756,919.47 Mg of CO 2 emissions. This research aims to highlight the critical role of satellite data in enhancing our understanding and development of comprehensive strategies for managing carbon stocks in terrestrial ecosystems.

Reducing Measurement Costs of Thermal Power: An Advanced MISM (Mamba with Improved SSM Embedding in MLP) Regression Model for Accurate CO2 Emission Accounting.

Current calculation methods for the carbon content as received (Car) of coal rely on multiple instruments, leading to high costs for enterprises. There is a need for a cost-effective model that maintains accuracy in CO2 emission accounting. This study introduces an MISM model using key parameters identified through correlation and ablation analyses. An Improved State-Space Model (ISSM) and an IS-Mamba module are integrated into a Multi-Layer Perceptron (MLP) framework, enhancing information flow and regression accuracy. The MISM model demonstrates superior performance over traditional methods, reducing the Root Mean Square Error (RMSE) by 22.36% compared to MLP, and by 9.65% compared to Mamba. Using only six selected parameters, the MISM model achieves a precision of 0.27% for the discrepancy between the calculated CO2 emissions and the actual measurements. An ablation analysis confirms the importance of certain parameters and the effectiveness of the IS-Mamba module at improving model performance. This paper offers an innovative solution for accurate and cost-effective carbon accounting in the thermal power sector, supporting China's carbon peaking and carbon neutrality goals.

Towards sustainable environment in North African countries: The role of military expenditure, renewable energy, tourism, manufacture, and globalization on environmental degradation.

Climate change resulting from increasing emissions has become a pressing concern in North African countries due to its significant environmental and socio-economic impacts. There is a need for extensive research on this topic to raise global awareness of the associated dangers. This study investigates the dynamic impact of economic growth, military expenditure, globalization, renewable energy, manufacturing, tourism, capital formation, and labor on CO2 emissions in North African countries from 1995 to 2021. The long-term results of the ARDL model reveal that globalization, renewable energy and capital formation have a negative impact on CO2 emissions, whereas economic growth, manufacturing, and tourism exhibit a positive impact. Pairwise Granger causality evidence indicates unidirectional causality from economic growth, globalization, military expenditure, manufacturing, tourism, and capital formation to CO2 emissions. These findings provide policymakers with critical insights to shape evidence-based interventions that promote renewable energy investments and globalization, enhance capital formation and high-skilled labor, and implement regulations to mitigate the environmental impacts of economic growth, military expenditure, manufacturing, and tourism. This guidance will help the region transition to a more environmentally friendly economic system.

Unlocking sustainable futures: Exploring the non-linear linkages between green finance and environmental sustainability in developing countries.

In an era characterized by growing environmental concerns and the urgent call for climate action, the role of green finance in addressing ecological sustainability has become a subject of paramount importance. Owing to this need, the current study examines the nonlinear relationship between green finance and environmental sustainability in 51 developing countries from 2000 to 2022. We employ various advanced panel estimation techniques, including Driscoll-Kraay standard errors (D-K), Feasible generalized least squares (FGLS), Generalized linear model (GLM), and System GMM to examine the long-run impact of this association. The results are further validated using a unique bootstrap quantile approach as a robustness check. We find a nonlinear inverted U-shaped relationship between green finance and environmental sustainability. We extend our analysis to lower-income and lower-middle-income countries and see that this nexus is stronger in middle-income countries than lower-income ones. Our findings also confirm the green finance based-EKC across the sub-samples of lower- and lower-middle-income countries. We also document that this nonlinear relationship is weak during the COVID-19 pandemic. This underscores the complexity of the investigated nexus, emphasizing the need for tailored strategies to foster a sustainable environment in developing economies.

Can forests realize the carbon neutrality dream? Evidence from a global sample.

The enlarge in economic activities and the urban population at the global level has brought about an increase in the demand for energy, food, and natural resources, as well as an exacerbation in global climate change concerns. In this respect, it is important to ensure the balance between global climate change and global economic activities. Therefore, a wide literature has emerged that searches for alternative solutions to improve climate change and carbon dioxide (CO2) emissions. The majority of existing studies emphasize the importance of renewable energy sources in environmental improvement efforts. Few studies highlight the importance of forestation in environmental improvement efforts, highlighting the non-linear effects of forestation. To fill this gap, this study uses panel data from 181 countries between 1990 and 2022 and evaluates the non-linear impact of economic growth, forest extent, energy efficiency, and urban growth on per capita CO2 emissions using a dynamic panel threshold and dynamic panel quantile threshold methods. Furthermore, we extend the model and conduct robustness tests examining the non-linear threshold effects of renewable and non-renewable energy consumption on per capita CO2 emissions. Our findings provide pieces of evidence that forest extents are an alternative solution to renewable energy use and energy efficiency in environmental improvement efforts.

How effective is business climate on CO2 emissions? A MMQR analysis for OECD countries.

Business climate measures regulations' impact on establishment, competition, and growth. In markets with high business climate levels, adopting new environmentally friendly and sustainable technologies and practices contributes to protecting the environment. For instance, a business climate that encourages the use of renewable energy sources like solar or wind power can significantly reduce CO2 emissions. This study analyzes the impact of business climate on CO2 emissions. The study uses data from 37 OECD countries, a widely recognized and reliable source, from 2007 to 2020. The Moments Quantile Regression (MMQR) method was preferred for data analysis due to its ability to handle non-normal distributions and outliers, ensuring robust results. According to MMQR estimates, business climate causes CO2 emissions to decrease. The effect of business climate on CO2 is negative up to the 90th quantile. According to MMQR estimates, the negative impact of business climate on CO2 has statistical significance up to the 80th quantile. Therefore, in all countries where CO2 levels are low or high, business climate helps protect the environment. However, the impact coefficients of business climate on CO2 are much higher than renewable energy. For this reason, it is essential for countries that want to reduce CO2 levels to consider the business climate level along with renewable energy.

Does FDI mitigate CO2 emissions intensity? Not when institutional quality is weak.

Foreign direct investment benefits developing countries. However, concerns have arisen that the influx of FDI potentially exacerbates environmental pollution. While this debate continues, growing attention has recently emerged on the role of institutions in mitigating FDI's potential damages, although the empirical findings remain inconclusive. This paper examines how institutional quality shapes the relationship between FDI and CO2, both at the aggregate level and across different income groupings, using a reduced-form CO2 emissions model, panel data from 2000 to 2018 and the IVGMM techniques. Three key conclusions emerge. First, the findings show that FDI reduces CO2 emissions, but its magnitude depends on the measure used. Second, institutional quality is directly associated with higher emissions across income groups, suggesting current regulations inadequately ameliorate environmental pollution. Third, we find a positive interaction effect between CO2 emissions and institutional quality. We argue that, for FDI to consistently curb CO2 emissions, the quality of institutions must improve to better regulate foreign investors' activities, especially in low and high-income nations. Enhancing the quality of institutions will help translate FDI into improved environmental outcomes across income groups.

Green growth in Africa: The role of renewable energy, biocapacity, government policies, and R&D on CO2 emissions reductions.

Africa's abundant natural resources and renewable energy potential offer long-term prosperity, but the continent is still challenged with several hurdles in exploiting these resources efficiently. This study examines the prospect for sustainable growth in Africa about the impacts of renewable energy, biocapacity, government policies, research and development (R&D), and population growth on CO2 emissions. By employing multiple advanced regression modeling techniques such as Dynamic Common Correlated Effects (DCCE), Common Correlated Effects Generalized (CCEG), and Bootstrap Quantile Regression (BSQR), the study analyzed the correlations between these variables using data from 19 African countries, spanning from 2000 to 2020. While the results showed renewable energy and bio-capacity to significantly reduce CO2 emissions in all countries, government policies and R&D expenditure show differentiated effects on CO2 emission across countries. Additionally, population growth was found to be a critical factor in exacerbating CO2 emissions in Africa. Observing the lack of connection between government policy and the taping of green potentials in Africa, the findings highlight the need for targeted government policies that can promote renewable energy infrastructure, protection of biocapacity through sustainable land use practices, and increased support of R&D on green technologies.

Toward a low carbon path: Do E-commerce reduce CO2 emissions? Evidence from China.

To address global climate change, achieving carbon peak and carbon neutrality has become a global consensus. However, the means to simultaneously achieve carbon reduction and promote green economic development, particularly in developing countries, require further investigation. This study evaluates the impact of e-commerce on CO2 emissions. Through an examination of the effects of the National E-Commerce Demonstration City (NEDC) policy from 2006 to 2017, this paper reveals that e-commerce growth facilitated by the NEDC policy resulted in a 7.89% reduction in total CO2 emissions and a per capita reduction of 1.1146 tons in the pilot cities. Mechanism analysis demonstrates that the upgrading of industrial structure, development of digital finance, and the growth of innovation and entrepreneurship serve as primary pathways for this impact. The robustness of the findings is supported by parallel trend tests, placebo tests, and additional sensitivity analyses. Furthermore, the research reveals that the NEDC policy exhibits a more significant reduction in CO2 emissions in cities with higher levels of economic development and non-resource-based cities. Welfare analyses show that the NEDC policy has significant socio-economic effects. These findings provide new evidence on the environmental effects of the digital economy and offer insights into achieving carbon neutrality.

Uncovering the drivers of CO2 emissions in the United States: The hidden spillover effects.

This study explores the applicability of the Environmental Kuznets Curve (EKC) hypothesis in the United States (US) from 2006 to 2020, employing the Spatial Durbin Model (SDM) to analyze the cross-border effects of pollution among states. The results indicate that although economic growth initially decreases environmental degradation, it subsequently contributes to more significant environmental degradation, challenging the EKC hypothesis's validity at the US state level. Factors such as higher energy prices and reliance on fossil fuels are also identified as significant drivers of environmental deterioration, with varying impacts observed across states. Conversely, adopting renewable energy sources is crucial in mitigating pollution levels. The study underscores the importance of coordinated state-level efforts to harmonize economic growth with sustainable environmental practices. It highlights the complexities of policymaking in balancing economic development with environmental conservation and emphasizes the need for targeted interventions to address environmental challenges effectively. This research enhances our understanding of sustainable development pathways amidst diverse regional dynamics within the US by providing empirical evidence and policy insights.

The impact of renewable energy, eco-innovation, and GDP growth on CO2 emissions: Pathways to the UK's net zero target.

In May 2019, the Climate Change Committee (CCC) recommended that the UK adopt a net-zero target, aiming to reduce its greenhouse gas emissions (GHG) by 100% from the 1990s baseline by 2050. The government accepted the recommendation, and the UK became the first major economy to establish a net-zero emissions law. To progress towards its climate objectives, the government took several initiatives, such as increasing its reliance on renewable energy sources and investing in climate mitigation technologies, which are commonly referred to as process eco-innovation. This study examines the impact of eco-innovation, process eco-innovation, renewable energy consumption, and economic growth on CO2 emissions in the UK using data from 1988 to 2020. We used the ARDL bound test with an error correction model (ECM) to examine the long-run and short-run cointegration between the variables of concern. We found that eco-innovation, process eco-innovation, and renewable energy consumption have significant roles in mitigating CO2 emissions, while economic growth contributes to environmental degradation in the UK. We also found that the effect of eco-innovation on CO2 emissions abatement is stronger than that of process eco-innovation in the short and long-run. Our robustness tests have confirmed the accuracy of those findings. In addition, the results from the Toda-Yamamoto causality revealed a one-way causality from process eco-innovation to CO2, renewable energy to CO2, and eco-innovation to CO2 emissions. Further, a bidirectional causality was found between GDP and CO2 emissions. The evidence presented in this paper provides great insight for shaping the energy policy in the UK and for establishing the climate budget in line with the country's net-zero target.

Role of energy transition in easing energy security risk and decreasing CO2 emissions: Disaggregated level evidence from the USA by quantile-based models.

Consistent with the increasing environmental interest, the clean energy transition is highly critical to achieving decarbonization targets. Also, energy security has become an important topic under the shadow of the energy crisis,. Accordingly, countries have been trying to stimulate clean energy use to preserve the environment and ensure energy security. So, considering the leading role of economic size and volume of energy use, the study examines the USA to define whether energy transition helps decrease energy security risk (ESR) and curb CO2 emissions. So, the study applies a disaggregated level analysis by performing quantile-based models for the period from 2001/Q1 through 2022/Q4. The results demonstrate that (i) the energy transition index decreases environmental ESR at higher quantiles and reliability ESR at lower and middle quantiles, whereas it is not beneficial in declining economic and geopolitical ESR; (ii) energy transition curbs CO2 emissions in building and transport sectors at lower quantiles, whereas it does not help decrease CO2 emissions in industrial and power sectors; (iii) energy transition is mostly ineffective on ESR, whereas it is highly effective in curbing CO2 emissions in all sectors except for transport across various quantiles as time passes; (iv) the results differ according to the aggregated and disaggregated levels; (v) the results are consistent across main and alternative models. Hence, the study highlights the dominant effect of energy transition in curbing sectoral CO2 emissions rather than easing ESR. Accordingly, the study discusses various policy implications for the USA.

From black to green: Quantifying the impact of economic growth, resource management, and green technologies on CO2 emissions.

In an exploration of environmental concerns, this groundbreaking research delves into the relationship between GDP per capita, coal rents, forest rents, mineral rents, oil rents, natural gas rents, fossil fuels, renewables, environmental tax and environment-related technologies on CO2 emissions in 30 highly emitting countries from 1995 to 2021 using instrumental-variables regression Two-Stage least squares (IV-2SLS) regression and two-step system generalized method of moments (GMM) estimates. Our results indicate a significant positive relationship between economic growth and CO2 emissions across all quantiles, showcasing an EKC with diminishing marginal effects. Coal rents exhibit a statistically significant negative relationship with emissions, particularly in higher quantiles, and mineral rents show a negative association with CO2 emissions in lower and middle quantiles, reinforcing the idea of resource management in emissions reduction. Fossil fuels exert a considerable adverse impact on emissions, with a rising effect in progressive quantiles. Conversely, renewable energy significantly curtails CO2 emissions, with higher impacts in lower quantiles. Environmental tax also mitigates CO2 emissions. Environment-related technologies play a pivotal role in emission reduction, particularly in lower and middle quantiles, emphasizing the need for innovative solutions. These findings provide valuable insights for policymakers, highlighting the importance of tailoring interventions to different emission levels and leveraging diverse strategies for sustainable development.

Financial regulations and sustainability: The role of energy price and climate policy uncertainty.

Russia ranks among the top five countries worldwide in terms of carbon emissions, with the energy, transportation, and manufacturing sectors as the major contributors. This poses a significant threat to both current and future generations. Russia faces challenges in achieving Sustainable Development Goal 13, necessitating the implementation of more innovative policies to promote environmental sustainability. Considering this alarming situation, this study investigates the role of financial regulations, energy price uncertainty, and climate policy uncertainty in reshaping sectoral CO2 emissions in Russia. This study utilizes a time-varying bootstrap rolling-window causality (BRW) approach using quarterly data from 1990 to 2021. The stability test for parameters indicates instability, suggesting that the full sample causality test may yield incorrect inferences. Thus, the BRW approach is employed for valid inferences. Our findings confirm the time-varying negative impact of financial regulations on CO2 emissions from energy, manufacturing, and transportation sectors. Additionally, findings confirm time-varying positive impact of energy prices and climate policy uncertainty on CO2 emissions from the energy, manufacturing, and transportation sectors. Strong financial regulations and stable energy and climate policies are crucial for achieving sustainability, highlighting significant policy implications for policymakers and stakeholders.