Level Of CO2 Emissions Research Articles

Colon capsule endoscopy: Can it contribute to green endoscopy?

Climate change due to sustained carbon dioxide (CO2) emissions poses a serious threat to human existence, such as extreme weather events that must be addressed in all sectors of society. Gastrointestinal endoscopy is a healthcare sector that produces high levels of CO2 emissions. Colonoscopy (CS) is the gold standard for colorectal cancer (CRC) screening that reduces the number of CRC-related deaths. However, vast amounts of cleaning solutions used to disinfect the colonoscope are disposed of, and multiple nonrenewable wastes are generated in performing CS, which significantly impact the environment. Currently, colon capsule endoscopy (CCE) retains good accuracy and has excellent potential to reduce CO2 emission through a simple procedure. However, to date, colon capsules are single-use and lack a clear collection pathway. In addition, there is a lack of specific data regarding the environmental impact of CCE. Further research is needed to prove that CCE is a more environmentally friendly tool than CS.

Effects of Primary Energy Consumption and Alternative Energy Patents on CO2 Emissions in China

China’s significant carbon emissions have attracted global attention, and the country has committed to reaching a peak in carbon emissions before 2030 and achieving carbon neutrality by 2060. It is crucial to achieve this goal by effectively controlling the combustion of primary fuels and developing alternative energy technologies. The existing literature has studied the effects of primary energy consumption on CO2 emissions, alternative energy technology on CO2 emissions, and energy patents on CO2 emissions. However, there are few studies on the effects of the relationship between primary energy consumption and alternative energy technology patents. This study analyzes the effects of primary energy consumption and alternative energy patents on CO2 emission intensity and CO2 emissions per capita, and their relationship using canonical correlation analysis. Our results are as follows. First, CO2 emissions from natural gas and liquefied petroleum gas have positive effects (correlation coefficients of 0.102 and 0.275, respectively), while CO2 emissions from gasoline, fuel oil, diesel, and kerosene have negative effects on CO2 emission intensity (correlation coefficients of −0.767, −0.420, −0.138, and −0.035, respectively). Second, patents for devices for producing mechanical power from muscle energy have large positive effects on total CO2 emissions (correlation coefficient of 0.533). Finally, the more the patents utilize waste heat, geothermal energy, hydro energy, and wind energy, the higher the CO2 emissions from liquefied petroleum gas, gasoline, and crude oil, and the lower the CO2 emissions from diesel, which are conducive to controlling CO2 emissions. Therefore, energy policies will be more effective, improve the living environment, and promote sustainable development based on the CO2 emissions level from primary energy consumption and the control degree of CO2 emissions by alternative energy.

Does Environmental Factor Influence the Rating of Creditworthiness? A Comparative Analysis of Developed versus Developing Countries

This paper examines the influence of environmental factor in the determination of a country’s creditworthiness given the world’s agenda to contain the rise in global temperature. This paper leverages on two environmental factor proxies, CO2 emissions per capita and renewable energy per capita, to assess whether the environmental factor plays a significant role in determining the sovereign credit ratings (SCRs) issued by three leading credit rating agencies (CRAs), i.e., Moody’s, S&P and Fitch for 49 countries spanning the period of 2000 to 2021. The empirical results show that the environmental factor is being considered by the CRAs. Since the signing of the Paris Agreement in 2015, the environmental factor has turned significant for the determination of the SCRs of developing countries, but not for the developed countries. The creditworthiness of developing countries is subjected to a penalty for CO2 emissions. While the level of renewable energy adoption is higher amongst the developed countries, the evidence does not show that their level of CO2 emissions is lower. This paper recommends the CRAs to explicitly state the environmental factor criteria and update their SCR methodologies to ensure uniformity in application.

Spatio-Temporal Analysis of CO2 Emissions from Vehicles in Urban Areas: A Satellite Imagery Approach

Carbon dioxide (CO2) emissions are a significant global environmental concern, widely notable as a major cause of climate change. Meanwhile, transportation is the main sector contributing to CO2 emissions, which are escalating at a faster rate than Gross Domestic Product Growth. This study attempted to evaluate the spatial–temporal pattern of CO2 emissions from vehicles using the Sentinel 5P satellite image. The Sentinel 5P image was acquired from the European Space Agency from 2019 until 2022. Utilizing ArcGIS 10.5, these data were analyzed to extract the CO2 values, which were then displayed as the total column amount. Thereafter, the extraction by point method was conducted on road features based on the Mukim Kajang basemap to obtain the value of CO2 emissions from transportation. Spatial–temporal mapping was then accomplished through kernel density analysis, enabling the identification of CO2 emission hotspot areas. The findings show that the spatial–temporal pattern of CO2 emissions was higher in September 2019 (0.06964 mol/m2), March 2020 (0.03596 mol/m2), December 2021 (0.0437 mol/m2), and January (0.03384 mol/m2), respectively. Based on eight cities in Mukim Kajang, Bandar Kajang has been a hotspot area for carbon dioxide emissions from motor vehicles for four consecutive years, starting in 2019 until 2022. In summary, the results of this study could provide guidelines to researchers and policymakers to develop effective strategies to reduce the level of CO2 emissions from transportation in urban areas.

A Step Toward Green Sustainability: Revisiting the Environmental Kuznets Curve Nexus in Light of the Forestry Trade

The question of how environmental degradation is linked with economic advancement is essential, particularly as climate change intensifies. The emerging literature tests this question in the Environmental Kuznets Curve (EKC) framework using macroeconomic variables; however, it often overlooks the EKC in the context of the forest products trade. The present study examines the presence of the EKC globally by investigating the impact of forest products trade on CO2 emissions from 1996 to 2022. For this purpose, this study employs the dynamic common correlated effects (DCCE) and augmented mean group (AMG) methods. The results confirm the presence of an inverted U-shaped EKC. The results show that the trade in forest products enhances the level of CO2. However, renewable energy and political stability help to reduce the level of CO2 emissions. The findings suggest that policymakers should consider sustainable green practices in forest productivity and green growth, which are essential for achieving long-term environmental goals.

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.

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.

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.

Numerical study on carbon emissions and economics of a high temperature heat pump system for an industrial process

Research to achieve net-zero is actively being carried out. In industrial processes, large amounts of carbon are emitted to product thermal energy, and there is a growing interest in electrification technologies to reduce this. While electric heaters and heat pumps are representative technologies for electrification, research to determine which technologies can economically contribute to carbon reduction is necessary. In this study, transient model for a heat pump and thermal energy storage (TES) was developed, and the CO2 emissions and economic feasibility were analyzed. When coupled with photovoltaic power and battery energy storage (BESS), it was found that the heat pump can reduce CO2 emissions more economically than electric heater. Transient analysis was performed for the case of coupling TES with heat pump instead of BESS and it was found that CO2 emissions vary from 276 to 231 g/kWhth with and without the TES, respectively. When combining the heat pump and photovoltaic system with TES or BESS, the nominal levelized cost of heat to reach the same level of CO2 emissions is 11.6 % higher for the BESS-coupled system. Up to certain level of CO2 emissions, the TES-coupled system is economically viable, but minimum emissions can be achieved with the BESS-coupled system.

Analysis of characteristics and mechanisms of iron-based oxygen carrier-aided oxy-fuel combustion of coal char in a bubbling fluidized bed

Oxygen carrier aided combustion (OCAC) technology, utilizing activated oxygen carriers within the fluidized bed material, aims to regulate oxygen distribution within the furnace and enhance the combustion environment through redox reactions with the macroscopic flow in the chamber. Oxy-fuel combustion is recognized as one of the mature technologies to capture CO2 from coal-fired power plants. Here, the effect of iron-based oxygen carriers (AR-Fe2O3, hematite, and steel slag) on the combustion characteristics of three coal chars (lignite, bituminous, and anthracite) are investigated in a bubbling fluidized bed operating at 850 °C and varying O2 concentrations ranging from 5 % to 21 % in CO2. The results indicate that the combustion rate of coal char and CO emission level are increased and the burnout time is shortened after replacing the quartz sand bed material with AR-Fe2O3. However, the combustion of lignite chars aided by hematite and steel slag exhibits opposite characteristics compared to those observed when substituting quartz sand bed material with AR-Fe2O3. With an increase in O2 concentration, the combustion behavior improved due to the increased mass transfer of O2 in CO2. The promoting effect of oxygen carrier on coal char combustion is in the following order: steel slag < hematite < AR-Fe2O3. The higher the fuel rank, the more obvious promoting effect of oxygen carrier. Analysis of OCAC mechanism combined with thermodynamic calculations shows that oxygen carrier serves a dual role: it provides oxygen to the fuel and simultaneously competes with the fuel for available oxygen. When coal char exhibits high reactivity, the competition for oxygen between the oxygen carrier and the fuel becomes more significant than the oxygen provision by the carrier. This heightened competition leads to an inhibition effect, as observed in the oxygen carriers aided combustion of lignite char.

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 to 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.

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.&amp;#xD;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.&amp;#xD;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.&amp;#xD;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.