Level Of CO2 Emissions Research Articles

Shadow Economy and Environmental Sustainability in Global Developing Countries: Do Governance Indicators Play a Role?

Environmental sustainability has been a challenging issue all over the globe, with air pollution posing a significant threat. One main factor contributing to air pollution is the growth of the shadow economies. This study investigates the effect of the shadow economy on air pollution and explores how these effects depend on the levels of governance indicators. We utilize key air pollution indicators: carbon dioxide (CO2) and nitrous oxide (N2O) emissions. Furthermore, we examine the role of key governance indicators: corruption control, the rule of law, and regulatory quality. The study utilizes an annual panel dataset of 107 selected developing countries worldwide, spanning from 2002 to 2020, and employs the System GMM technique, which effectively tackles the omitted variable bias, potential endogeneity, and simultaneity issues in the model. The estimation results indicate that a sizeable shadow economy significantly increases the levels of CO2 and N2O emissions. Moreover, the results reveal that robust governance frameworks, evidenced by enhanced corruption control, a stronger rule of law, and superior regularity quality, mitigate the adverse effects of the shadow economy on CO2 and N2O emissions. This highlights a significant substitutability between the shadow economy and governance indicators, indicating that improvements in governance formworks will not only reduce the size of the shadow economy but also weaken its harmful impact on air pollution. Policy initiatives should thus focus on strengthening governance mechanisms, particularly enhancing control of corruption and the rule of law to effectively reduce the environmental impact of the shadow economies in developing countries. Additionally, governments should prioritize reforms in regulations and legal frameworks to limit the expansion of the shadow economy, thereby decreasing their contribution to air pollution.

Analysis of the Impact of Selected Dynamic Parameters of a Motor Vehicle on CO2 Emissions Using Logistic Regression

The article analyzes the impact of selected operational parameters of internal combustion engine vehicles on CO2 emissions. The study was preceded by a detailed analysis of the issues related to CO2 emissions in the EU, with a focus on Poland, where the tests were conducted. The key scientific assumption is that individual vehicle users’ behaviors significantly impact global CO2 emissions. Daily use of private vehicles, driving style, and attention to fuel efficiency contribute to cumulative effects that can drive the transformation toward more sustainable transport. Therefore, the study was conducted using real-time empirical data obtained from the vehicles’ OBD (On-Board Diagnostics) diagnostic systems. This approach enabled the creation of a diagnostic tool allowing each vehicle user to assess CO2 emissions and ultimately manage its levels, which is the biggest innovation of the work. Two levels of CO2 emissions were identified as categorical variables in the model, considered either ecological or non-ecological from the perspective of sustainable transport. The CO2 emission threshold of 200 g/km was adopted based on the average age of vehicles in Poland (14.5 years) and Regulation (EC) No 443/2009 of the European Parliament and of the Council. Three models of logistic regression dedicated to different driving cycle phases—starting, urban driving, and highway driving—were proposed and compared. This study demonstrated that during vehicle starting, the most significant factors influencing the probability of ecological driving are vehicle velocity, relative engine load, and relative throttle position, while for the other two types of movement, engine power and torque should also be considered. The logistic regression model for vehicle start-up obtained a value of sensitivity at about 82% and precision at about 85%. In the case of urban driving, the values of the discussed parameters reach significantly higher levels, with sensitivity at around 96% and precision at about 92%. In turn, the model related to highway driving achieved the highest values among the created models, with sensitivity at around 97% and precision at about 93%.

Great Gerbils (Rhombomys opimus) in Central Asia Are Spreading to Higher Latitudes and Altitudes.

The great gerbil (Rhombomys opimus) is a gregarious rodent in Central Asia and is one of the major pests found in desert forest and grassland areas. The distribution changes and migration routes of R. opimus in Central Asia under climate change remain unexplored. This study employed multi-model ensemble, correlation analysis, jackknife method, and minimum cumulative resistance (MCR) model to simulate the potential habitat of R. opimus under current and future (2030 and 2050) climate scenarios and estimate its possible migration routes. The results indicate that the ensemble model integrating Random Forest (RF), Gradient Boosting Machine (GBM), and Maximum Entropy Model (MaxEnt) performed best within the present climate context. The model predicted the potential distribution of R. opimus in Central Asia with an area under the curve (AUC) of 0.986 and a True Skill Statistic (TSS) of 0.899, demonstrating excellent statistical accuracy and spatial performance. Under future climate scenarios, northern Xinjiang and southeastern Kazakhstan will remain the core areas of R. opimus distribution. However, the optimal habitat region will expand relative to the current one. This expansion will increase with the rising CO2 emission levels and over time, potentially enlarging the suitable area by up to 39.49 × 104 km2. In terms of spatial distribution, the suitable habitat for R. opimus is shifting toward higher latitudes and elevations. For specific migration routes, R. opimus tends to favor paths through farmland and grassland. This study can provide guidance for managing and controlling R. opimus under future climate change scenarios.

Determinants and effects of climate finance in the transport sector

We examine the determinants of climate-related development finance flows to the transportation sector and their relationship with CO2 emission levels. The mitigation fund allocation is particularly correlated with the demographic characteristics of recipient countries; however, the correlation with per capita income and emissions seems to be weak. In contrast, equity considerations appear to be the main determinants of adaptation fund allocation. Finally, we find some evidence of a significant negative relationship between mitigation investments and emissions per capita and while the overall effect is modest, it tends to be most marked in countries with the highest per capita emissions. Thus, the fact that countries with the highest emissions per capita are not receiving relatively more investments may be hampering the effectiveness of climate finance.

Assessing the Impact of CO2 Emissions from Transport and Power on Health and Environment in Duhok, Kurdistan Region, Iraq

This study investigates the carbon dioxide (CO2) emissions generated by transportation and power generation in Duhok Governorate, Kurdistan Region of Iraq. The research highlights the alarming levels of CO2 emissions resulting from the region's 270,315 vehicles and 1,814 private electric power generators, which consume approximately 1,500,000 liters of gasoline and 650,000 liters of diesel daily. These activities contribute to a total daily CO2 emission of 1,741,188 kg, amounting to over 626,827,680 kg annually. The findings reveal the significant environmental and public health threats posed by these emissions, emphasizing the urgent need for targeted mitigation strategies. Comparisons with other urban regions such as Cairo, Tehran, New Delhi, Lagos, and Nairobi reveal a similar pattern of high vehicular and generator-induced CO2 emissions, underscoring the global nature of this environmental challenge. The study underscores the importance of addressing CO2 emissions through improved regulations, modernized infrastructure, and sustainable energy practices to protect both the environment and public health.

Car Sales, Fuel Economy and Decarbonization in Mexico

The car market in Mexico has undergone substantial change over the last twenty years, as sales have increased dramatically and as policy measures have been introduced to improve fuel economy so that decarbonization targets can be achieved. The argument presented in this paper is that overall fuel economy is driven by vehicle sales and the fuel economy standards imposed. In addition, this picture is complicated by the purchasing preferences of buyers, and this might reduce the effectiveness of the policy measures introduced. A case study approach allows analysis of the shifts in transport policy (2003–2020) to be undertaken by linking the fuel consumption of cars (L/100 km) to the purchasing patterns of consumers, and from this estimate the levels of CO2 emissions. From the empirical analysis, it is found that, although there have been fuel economy gains every year, this is countered by (a) increasing sales of SUVs, and (b) a car market that is increasingly being dominated by larger cars. The current fuel standards are not sufficient to control the continued growth in fuel consumption, and levels of carbon emissions are continuing to increase. In conclusion, tighter emissions standards are needed, together with stronger governance structures and a range of further policy measures to improve car efficiencies and limit growth of the use of larger vehicles.

Advancing carbon neutrality in post-COP28 in Ghana: Examining the impact of foreign direct investment and governance indicators

This paper investigates the efficacy of foreign direct investment (FDI) and governance quality in reducing the adverse impact of CO2 emissions in Ghana. To achieve this aim, panel data for the independent and control variables spanning from 2000-2022 were obtained from World Development Indicators database and World Governance Indicators. The data was analysed using the Fixed Generalised Least Squares (FGLS) and the Method of Moment Quantile Regression (MMQR) methods. The findings of the study reveal that FDI aids in lowering the level of CO2 emissions in Ghana and therefore can be achieved through political stability, voice and accountability, controlled corruption, and enforcement of rule of law. It further reveals that the level of governance effectiveness and regulation quality is low in attracting FDI to aid in lowering CO2 emissions in Ghana. This study suggests that policymakers in Ghana should strengthen the system and structure governing the environmental management process and improve the quality of environmental policies to encourage environmentally friendly FDI flows.

Signal-devices management and data-driven evidential constraints based robust dispatch strategy of virtual power plant

Ensuring the safety and reliability of energy systems is very important in power grid operation. However, inherent intricacies and uncertainties of modern-day power systems, such as the mismatch between signal frequency and equipment response speed and the stochasticity associated with renewable energy and load forecasts, create significant challenges for generation dispatch planning. This study proposes a robust optimization approach with constraints based on signal-devices management and data-driven evidential distribution constraints. The first stage of this approach is to decompose and recombine the net power demand according to the Hilbert-Huang transform and device capability. The signal-device management constraints are then established based on this. The second stage formulates an evidential constraint based on data-driven evidential distribution and chance constraints in a distributionally robust optimization framework that caters to the stochasticity associated with renewable energy and load forecasts. The proposed model is validated on a virtual power plant to assess the approach’s efficacy for different dispatching scenarios. Penalty-based sensitivity analysis provides further insights into the proposed method’s performance for varying levels of CO2 emissions. Simulation results demonstrate that system flexibility becomes increasingly crucial for maintaining system stability and security as the penetration of renewable energy grows. Compared with chance constraint, the proposed data-driven evidential constraint effectively enables the optimization framework to handle stochasticity after sacrificing 0.62% more economic loss and 0.7% more environmental loss. Excessively high penalty parameters for CO2 do not promote economic development, resulting in 21.08% and 52.51% more economic and environmental losses without obvious environmental protection.

MSCL-Attention: A Multi-Scale Convolutional Long Short-Term Memory (LSTM) Attention Network for Predicting CO2 Emissions from Vehicles

The transportation industry is one of the major sources of energy consumption and CO2 emissions, and these emissions have been increasing year by year. Vehicle exhaust emissions have had serious impacts on air quality and global climate change, with CO2 emissions being one of the primary causes of global warming. In order to accurately predict the CO2 emission level of automobiles, an MSCL-Attention model based on a multi-scale convolutional neural network, long short-term memory network and multi-head self-attention mechanism is proposed in this study. By combining multi-scale feature extraction, temporal sequence dependency processing, and the self-attention mechanism, the model enhances the prediction accuracy and robustness. In our experiments, the MSCL-Attention model is benchmarked against the latest state-of-the-art models in the field. The results indicate that the MSCL-Attention model demonstrates superior performance in the task of CO2 emission prediction, surpassing the leading models currently available. This study provides a new method for predicting vehicle exhaust emissions, with significant application prospects, and is expected to contribute to reducing global vehicle emissions, improving air quality, and addressing climate change.

Integrating Fuzzy MCDM Methods and ARDL Approach for Circular Economy Strategy Analysis in Romania

This study investigates the factors influencing CO2 emissions in Romania from 1990 to 2023 using the Autoregressive Distributed Lag (ARDL) model. Before the ARDL model, we identified a set of six policies that were ranked using Fuzzy Electre, Topsis, DEMATEL, and Vikor. The multi-criteria decision-making (MCDM) methods have highlighted the importance of a circular policy on CO2 emission reduction, which should be a central focus for policymakers. The results of the ARDL model indicate that, in the long term, renewable energy production reduces CO2 emissions, showing a negative relationship. Conversely, an increase in patent applications and urbanization contributes to higher CO2 emissions, reflecting a positive impact. In total, five key factors were analyzed: CO2 emissions per capita, patent applications, gross domestic product, share of energy production from renewables, and urbanization. Notably, GDP does not significantly explain CO2 emissions in the long run, suggesting that economic growth alone is not a direct driver of CO2 emission levels in Romania. This decoupling might result from improvements in energy efficiency, shifts towards less carbon-intensive industries, and the increased adoption of renewable energy sources. Romania has implemented effective environmental regulations and policies that mitigate the impact of economic growth on CO2 emissions.

Climate change mitigation policy for planetary health equity? An automated content analysis of countries’ nationally determined contribution reports

Abstract Introduction This study examines the extent to which national governments discuss the social determinants of planetary health equity (SDPHE) within their Nationally Determined Contribution reports (NDCs) to the UN Framework Convention on Climate Change. This is assessed relative to the frequency of discussion of economic factors and health outcomes, and how this varies between countries based on political, economic, and environmental factors.
Methods Using natural language processing, a dictionary-based automated content analysis was conducted of the frequency of terms within these reports. Correlation analyses examined the relationship between the frequency of dictionary categories and political, economic, and environmental variables to ascertain the role of contextual factors.
Results Overall, NDCs were found to feature a greater proportion of economic language compared to health outcome and SDPHE terms. Among the SDPHE, equity- and gender-related terms occurred most frequently. Correlations were identified primarily among high-income countries, that suggest moderate positive associations between levels of CO2 emissions per capita and per dollar of GDP and the use of economic terms, and a negative association of economic language with levels of democracy. Democracy was also positively associated with language related to social norms such as equity and justice, indicating potential scope for impact through democratic pressures.
Conclusion The relatively frequent use of economic frames in NDCs suggests that economic issues may receive more attention compared to the SDPHE. This analysis identifies potential enabling and constraining country-level factors for greater attention to the SDPHE in NDCs and more progressive climate change mitigation policymaking.

CO2 Hydrogenation on Ru Single-Atom Catalyst Encapsulated in Silicalite: a DFT and Microkinetic Modeling Study.

The critical levels of CO2 emissions reached in the past decade have encouraged researchers into finding techniques to reduce the amount of anthropogenic CO2 expelled to the atmosphere. One possibility is to capture the produced CO2 from the source of emission or even from air (i.e., direct air capture) by porous materials (e.g., zeolites and MOFs). Among the different usages of captured CO2, its conversion into light fuels such as methane, methanol, and formic acid is essential for ensuring the long-awaited circular economy. In the last years, single-atom catalysts encapsulated in zeolites have been considered to this purpose since they exhibit a high selectivity and activity with the minimum expression of catalytic species. In this study, a detailed mechanism composed by 47 elementary reactions, 42 of them in both forward and reverse directions and 5 of them that correspond to the desorption of gas products just forwardly studied), has been proposed for catalytic CO2 hydrogenation over Ru SAC encapsulated in silicate (Ru1@S-1). Periodic density functional theory (DFT) calculations along with microkinetic modeling simulations at different temperatures and pressures were performed to evaluate the evolution of species over time. The analysis of the results shows that carbon monoxide is the main gas produced, followed by formic acid and formaldehyde. The rate analysis shows that CO(g) is formed mainly through direct dissociation of CO2 (i.e., redox mechanism), whereas COOH formation is assisted by OH. Moreover, the Campbell's degree of rate control analysis suggests that the determining steps for the formation of CO(g) and CH2O(g) gas species are their own desorption processes. The results obtained are in line with recent experimental and theoretical results showing that Ru1 SACs are highly selective to CO(g), whereas few atom clusters as Ru4 increase selectivity toward methane formation.

The impact of renewable energy consumption and energy poverty on pollution in Central and Eastern European countries

Considering the actual climate challenges, pollution should be reduced in Central and Eastern European countries (CEECs) from the European Union (EU). The main objective of this paper is to assess the impact of energy poverty and renewable energy consumption (REC) on pollution in 10 CEE countries, EU member states, during 2005–2022. The results based on the synthetic control method suggested that Renewable Energy Directive implemented in 2009 contributed to the decrease in the level of CO2 emissions in each CEEC compared to the states in this region that are not EU member states (Albania, Serbia, Moldova, North Macedonia, Georgia, Montenegro, Belarus, Ukraine). The panel regression analysis based on MG estimator showed that energy poverty enhanced CO2 emissions, while renewable energy consumption reduced it and had a mediating effect on the impact of energy poverty on pollution in the CEECs. The population unable to keep home warm, foreign direct investment and value added in industry had an asymmetric effect on CO2 emissions in the CEECs as method of moments quantile regressions suggest. These results are subject to policy proposals to tackle both pollution and energy poverty in the region.

Climate change and greenhouse gas emissions from soils under the winter wheat agroecosystem in Ukraine

The issue of global climate change is one of the most important challenges for modern environmental science. In this regard, assessment of greenhouse gas emissions from soils under agroecosystems is an extremely important task. The main purpose of the study was to assess the potential emissions of greenhouse gases (CO2 and N2O) from the soils under the winter wheat agroecosystem during its spring-summer growing season under the climate change in Ukraine. A comprehensive model of greenhouse gas emissions from agroecosystem soils was used as a theoretical basis. The study considered the main agroclimatic regions of Ukraine, such as Polissia, Forest-Steppe, Northern Steppe, and Southern Steppe. The modelling was performed for average long-term conditions of 1981–2020 and under scenario conditions (using the RCP4.5 climate scenario) for the decades of 2021–2030, 2031–2040, and 2041–2050. Estimates of CO2 emissions were obtained for the agroclimatic regions of Ukraine. According to the results, the largest CO2 emissions (0.886-0.940 t C-CO2/ha) will be expected when the average productive moisture supplies in the arable soil layer for the spring-summer period of winter wheat vegetation are 20–40 mm and the air temperature is 12–15 °C. Rise in the level of moisture reserves, averaged for the period, for more than 40 mm and the air temperature of 11–12 °C is accompanied by decrease in the level of CO2 emission (0.645–0.658 t C-CO2/ha). In arid conditions, with a decrease in the period-average moisture supplies in the arable soil layer to less than 20 mm and heightened average air temperatures (14.5–15.2 °C), a decrease in CO2 emissions is expected (0.755–0.770 t C-CO2/ha). The study showed that the highest N2O emission (1.087–1.703 kg N-N2O/ha) is expected at a high level of moistening (Md being equal to 0.474–0.713 relative units and air temperature – 11.3–13.0 °C) with a high intensity of nitrification and denitrification processes. Conditions characterizing the average level of N2O emission (0.657–0.890 kg N-N2O/ha) are formed under the moistening at a level of Md equaling 0.296–0.447 relative units and air temperature of 13.4–14.7 °C. There is a slowdown in denitrification processes and an increase in the share of emissions owing to nitrification. It has been found that the lowest N2O emission (0.508–0.537 kg N–N2O/ha) is expected under conditions of severe and very severe drought (according to the Md criterion), with a heightened temperature averaged at the level of 14.5–15.2 °C during the spring-summer period of winter wheat vegetation.

Effect of Separating Air into Primary and Secondary in an Integrated Burner Housing on Biomass Combustion

Pellet burners, although they are commonly used devices, require high-quality fuels and yet are characterized by relatively high levels of CO and NO emissions and their variability. This article presents a combustion study of an original biomass burner that separates air into primary for biomass gasification and secondary for oxidizing the gasification products, with ducts placed in the housing of the burner. This study introduces a new burner design that separates air into primary and secondary streams within an integrated burner housing, aiming to optimize biomass combustion efficiency and reduce harmful emissions. Two burner designs were proposed, with a high secondary air nozzle (HCrown) and a low secondary air nozzle (LCrown). These two burners were compared with a typical retort burner (Ret). The LCrown burner reduced particulate matter emissions by 36% and CO emissions by 74% with respect to a typical retort burner. This study showed that the distance of the secondary air nozzles from the gasifying part has a significant impact on the operation of the burner and the possibility of reducing emissions of CO and NO. These results highlight the potential of the innovation to significantly improve combustion quality while simultaneously reducing environmental impact.

Estimation of carbon stocks and CO2 emissions resulting from the forest destruction in West Kalimantan, Indonesia

The issue of climate change has become a global concern, and tropical regions are strategically positioned to mitigate its effects through their forest resources. By maintaining the function of forest ecosystems to support life at local and regional levels, carbon stocks can be maintained, and emissions can be reduced. Above-ground biomass can bind and store carbon stocks. This study aims to analyse the CO2 emissions caused by deforestation and forest degradation in Kubu Raya District, West Kalimantan Province. Primary data was obtained through field observations, and secondary data was obtained through maps of forest areas and land cover for 1996–2021 from the Geospatial Information Agency and the Indonesian Ministry of Environment and Forestry. This study uses a non-destructive approach to calculate Above Ground Biomass. The results showed that Kubu Raya District, West Kalimantan Province, Indonesia, experienced deforestation of 235,597 ha and forest degradation of 9,860 ha from 1990 to 2021. This resulted in CO2 emissions of 163,702,815.53 tons. The highest level of CO2 emissions is in Batu Ampar District, and the lowest is in Sungai Kakap District. There is a positive correlation between the amount of deforestation and forest degradation and the level of CO2 emissions caused by these activities. This means that the amount of CO2 emissions depends on the area's deforestation and forest degradation. To reduce the concentration of CO2 in the atmosphere and its effects on climate change, deforestation and forest degradation activities in Kubu Raya Kalimantan should be stopped.

High-resolution mapping of carbon dioxide emissions in Guizhou Province and its scale effects

Accurate spatial distribution of carbon dioxide (CO2) emissions is essential information needed to peaking emissions and achieving carbon neutral in China. The aim of this study was to map CO2 emissions with high spatial resolution at provincial scale and then explore the scale effect on mapping results. As an example, the spatiotemporal pattern and factors influencing CO2 emissions were examined in Guizhou Province in Western China. With the proposed method, a reasonable spatial distribution of CO2 emissions with high spatial resolution was obtained, which had relatively accurate information on spatial details. The optimal resolution of CO2 emissions at the provincial scale under high spatial resolution was approximately 90 m and 1260 m. More detailed grid data can better reflect the spatial variability of CO2 emissions. Emissions of CO2 were spatially heterogeneous in Guizhou, with high emissions in centers of big cities that gradually spread and decreased from city centers. From 2009 to 2019, the spatial distribution of CO2 emissions developed from agglomeration to dispersion. Areas of high carbon emissions decreased, those of medium carbon emissions increased, and many areas changed from no emissions to carbon emissions. Industrial land had the highest emissions, followed by commercial and transportation lands. Over 10 years, changes occurred in the relation between interregional economic level of Guizhou and CO2 emissions, with the relation changing from linear into an inverted U-shaped relation. The effect of industrial structure on CO2 emissions decreased, and the linear increase between CO2 emissions and the urban scale became more evident. The results of this study will contribute to accurate monitoring and management of carbon emissions in Guizhou, as well as provide support to formulate policies related to controls on carbon emissions in different regions.

Carbon balance in Maize (Zea mays L.) with various combinations of organic and inorganic fertilizers

Fertilization in agricultural cultivation has an important role in the carbon cycle. This study aims to evaluate using organic fertilizers with inorganic fertilizers that can produce high carbon sequestration with low CO2 emission levels in maize (Zea mays L.) cultivation. There were seven combinations of NPK and organic fertilizers, namely ¼ NPK + 1 OF (C), ½ NPK + 1 OF (D), ¾ NPK + 1 OF (E), 1 NPK + 1 OF (F), ¾ NPK + ¼ OF (G), ¾ NPK + ½ OF (H), ¾ NPK + ¾ OF (I), and two controls, namely no fertilizer (A) and standard NPK (350 kg/ha, SP36 150 kg/ha, KCl 75 kg/ha) (B). Organic fertilizer was applied one week before planting, SP-36 and KCl fertilizer at planting, and urea fertilizer at 0, 14, and 28 HST. Maize was planted with a spacing of 20 x 70 cm. Parameters observed included CO2 emissions, soil pH, C-Organic, C-microbial, and plant growth parameters. The combined use of NPK and organic fertilizer significantly increased soil carbon stocks (33.25-64.04 Mg/ha) and carbon sequestration by plants (3.76-5.98 Mg/ha). Therefore, using organic and inorganic fertilizer can be considered more environmentally friendly and effective in managing carbon balance on farmland and has great potential to contribute to climate change mitigation through increased soil and plant carbon sequestration.

Steel solutions for a sustainable bridge infrastructure of today and tomorrow

AbstractResponsible for 70% of the global CO2 emissions, infrastructure has a significant impact on our climate and the related consequences. Bridges are a major keystone of the infrastructure and take a leading position in its decarbonization. In this framework, low‐carbon emission steels play an important role. Low‐carbon emission steels, like ArcelorMittal's XCarb® recycled and renewably produced steels, are already available on the market. Combining scrap and renewable electricity, it offers very low levels of CO2 emissions per ton of finished steel. The present paper describes how different steel solutions can be combined to lower the material input, to simplify the construction process and to reduce the maintenance efforts in bridge construction. The impact of efficient design has been assessed by a Life‐Cycle Assessment (LCA) for bridge solutions with small and medium spans of up to 40 m. The paper concludes with a summary of recently realized bridge projects in Poland.

Facile Synthesis of Porous Zinc Nanosheets for Highly Selective Electrochemical CO2 Reduction to CO

AbstractWith the increasing levels of CO2 emissions and the decreasing availability of energy resources, the electrocatalytic CO2 reduction reaction (CO2RR) offers a promising method for utilizing CO2 as a valuable resource using sustainable electrical energy. However, developing cost‐effective and highly efficient catalysts remains a significant challenge. Although zinc, an abundant element on Earth, has shown potential for converting CO2 into CO, its effectiveness as a catalyst for CO2 reduction is hindered by its low selectivity and stability. This study introduces a fast, efficient, and simple electrochemical deposition method for preparing porous zinc nanosheets (NS) using the hydrogen bubble template approach. The as‐prepared zinc NS catalyst demonstrates exceptional catalytic activity due to its highly porous structure and high specific surface area. At a voltage of −1 V versus RHE, it achieves a maximum CO Faradaic efficiency of 89.5 %. The performance of this system is characterized by a partial current density of around −6.17 mA cm−2 in an H‐cell configuration. Additionally, our catalyst exhibits remarkable durability over 10 h, highlighting its significant potential for real‐world utilization in CO2RR.