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Environmental issues in energy policy, especially global warming, have received more attention lately than ever before. Excessive dependence on fossil fuels, deforestation, and land degradation are the three main factors that lead to increased carbon dioxide (CO2) emissions. Consequently, the global average temperature has doubled compared to anticipation. Various international protocols and agendas have been established, pledged to restore the global average temperature to the 1990 level. As a result, energy policies worldwide have also undergone various transformations to align with these protocols since then. As a developing nation, Malaysian’s electricity demand has continuously grown in the past two decades. To date, the electricity sector is still dominated by fossil fuels. Government incentives have been the most influential factor in the nation’s energy mix trend. Several energy policies implemented throughout the past 22 years have seen the shift from natural gas to coal power in power plants, and in more recent years, renewable energy resources. Numerous studies in the past have independently outlined the status of various energy source in Malaysia. However, they all fell short in providing the greenhouse gas (GHG) emissions in the Malaysian energy sector. Notably, the question that remains to be answered is how GHG emissions have changed in response to the amendment in the energy mix; hence, the effectiveness of policy change in this aspect remains unknown. This paper analysed the past and present trend of Malaysia electricity generation mix and the resultant GHG emissions. In particular, this paper focused on investigating the variation of combined specific GHG emissions in the Malaysian electricity sector, in response to the policy change within the past 22 years. This provides the insight for Malaysian policymakers to evaluate the effectiveness of past policies in GHG emissions and the measures to be taken in future. The finding of this paper shows the attention on the nation’s GHG emissions has evolved over the years, following the diversification in energy mix driven by the policy change. It was also found that, on average, it took a decade for a significant reduction in specific GHG emission to be visible since the government’s energy policy implementation.

Sierra Leone is suffering from a persistent electricity gap that has crippled its economic growth and prevented it from attaining several health and education development goals. This persistent electricity gap has generated significant interest in tackling the country’s long-lasting energy deficiency. Providing electricity in a reliable, sustainable, and cost-effective manner in Sierra Leone requires adopting robust integrated energy planning and appropriate technologies. Despite various interventions by the government, a balance between electricity demand and supply has yet to be achieved. Using the Long-range Energy Alternatives Planning System (LEAP), this work assesses Sierra Leone’s energy supply and demand for 2019–2040. We developed three case scenarios (Base, Middle, and High) based on forecasted demand, resource potential, techno-economic parameters, and CO2 emissions. The Base case considers the electricity sector as business as usual, the Middle case examines the electricity sector reform roadmap and the prospect of integrating renewable energy into the power system, and the High case examines the sustainable development of the power generation system considering the electricity sector roadmap. As part of this study, we analyze potential alternatives to conventional electricity generation systems aimed at providing electricity in a sustainable, reliable, and affordable manner, including the use of renewable energy sources and technologies with less CO2 emissions. Model results estimate an increase in electricity demand of 1812.5 GWh, 1936 GWh, and 2635.8 GWh for Base, Middle, and High cases respectively. Also, there is a reduction in production, fuel cost, and CO2 emission in the High case to the Base case by 67.15%, 35.79%, and 51.8%, respectively. This paper concludes with recommendations devised from the study results for the power system of Sierra Leone.

Greenhouse gas (GHG) emissions from agriculture contribute to climate change. The consequences of unsustainable agricultural activity are polluted water, soil, air, and food. The agricultural sector has become one of the major contributors to global GHG emissions and is the world’s second largest emitter after the energy sector, which includes emissions from power generation and transport. Latvian and Lithuanian agriculture generates about one fifth of GHG emissions, while Estonia generates only about one tenth of the country’s GHG emissions. This paper investigates the GHG trends in agriculture from 1995 to 2019 and the driving forces of changes in GHG emissions from the agricultural sectors in the Baltic States (Lithuania, Latvia, and Estonia), which are helpful for formulating effective carbon reduction policies and strategies. The impact factors have on GHG emissions was analysed by using the Logarithmic Mean Divisia Index (LMDI) method based on Kaya identity. The aim of this study is to assess the dynamics of GHG emissions in agriculture and to identify the factors that have had the greatest impact on emissions. The analysis of the research data showed that in all three Baltic States GHG emissions from agriculture from 1995 to 2001–2002 decreased but later exceeded the level of 1995 (except for Lithuania). The analysis of the research data also revealed that the pollution caused by animal husbandry activities decreased. GHG intensity declined by 2–3% annually, but the structure of agriculture remained relatively stable. The decomposition of GHG emissions in agriculture showed very large temporary changes in the analysed factors and the agriculture of the Baltic States. GHG emissions are mainly increased by pollution due to the growing economy of the sector, and their decrease is mainly influenced by two factors—the decrease in the number of people employed in the agriculture sector and the decreasing intensity of GHGs in agriculture. The dependence of the result on the factors used for the decomposition analysis was investigated by the method of multivariate regression analysis. Regression analysis showed that the highest coefficient of determination (R2 = 0.93) was obtained for Estonian data and the lowest (R2 = 0.54) for Lithuanian data. In the case of Estonia, all factors were statistically significant; in the case of Latvia and Lithuania, one of the factors was statistically insignificant. The identified GHG emission factors allowed us to submit our insights for the reduction of emissions in the agriculture of the Baltic States.

Electric vehicles represent a promising opportunity to achieve greenhouse gas (GHG) reduction targets in the transport sector. Integrating them comprehensively into the energy system requires smart control strategies for the charging processes. In this paper we concentrate on charging processes at the end users home. From the perspective of an end user, optimizing of charging electric vehicles might strive for different targets: cost minimization of power purchase for the individual household or—as proposed more often recently—minimization of GHG emissions. These targets are sometimes competing and cannot generally be achieved at the same time as the results show. In this paper, we present approaches of considering these targets by controlling charging processes at the end users home. We investigate the influence of differently designed optimizing charging strategies for this purpose, considering the electrical purchase cost as well as the GHG emissions and compare them with the conventional uncontrolled charging strategy using the example of a representative household of a single family. Therefore, we assumed a detailed trip profile of such a household equipped with a local generation and storage system at the same time. We implemented the mentioned strategies and compare the results concerning effects on annual GHG emissions and annual energy purchase costs of the household. Regarding GHG emissions we apply a recently proposed approach by other authors based on hourly emission factors. We discuss the effectivity of this approach and derive, that there is hardly no real impact on actual GHG emissions in the overall system. As incorporating this GHG target into the objective function increases cost, we appraise such theoretical GHG target therefore counterproductive. In conclusion, we would thus like to appeal for dynamic electricity prices for decentralised energy systems, leading at the same time to cost efficient charging of electric vehicles unfolding clear incentives for end users, which is GHG friendly at the end.

Efforts to improve air quality and concerns about global warming make transportation mediums that do not produce emissions more attractive to end users. Meanwhile, some of these transportation mediums are powered by an electricity grid that generates a great deal of emissions. This study compared the greenhouse gas GHG emissions for both electric and fossil-powered vehicles using estimates of tailpipe emissions of fossil-powered vehicles and the indirect emissions from the electricity grid. Furthermore, a system dynamic model was developed for a more holistic review of the GHG emissions for both electric and fossil-powered vehicles. The result indicated that in terms of associated emissions from the grid, electric-powered vehicles are not always better than fossil-powered vehicles when the electricity is not from a renewable source. The GHG emissions for electric-powered vehicles are dependent on both the electricity usage rate of the vehicle and the GHG emissions that are associated with the production of that amount of electricity. Further opportunities exist in renewable and clean energy technologies for various operations. Based on reports from previous works, this report also presented potential strategies to achieve a significant reduction in GHG emissions for both the electricity grid and fossil fuel refining processes.

Understanding the underlying reasons for greenhouse gas (GHG) emissions trends in different countries is fundamental for climate change mitigation. This paper identifies the main determinants that affect GHG emissions growth and assesses their impact and differences among countries in Europe. Previous studies have produced inconclusive results and presented several limitations, such as the lack of quality of the data used, the reduced identification of determinants and the use of methods that did not enable hypothesis testing. Conversely, this research identifies an extended list of determinants of GHG emissions, performs an in-depth statistical analysis and contrasts the significance of determinants using panel data and multiple linear regression models for the period 1990–2017 for the main Eurozone countries. The study found that GDP and final energy intensity are the main drivers for the reduction of GHG emissions in Europe. Furthermore, energy prices are not significant and heterogeneous results are found for the renewable energy, fuel mix and carbon intensity determinants, pointing to a different behavior at the country level. The uneven impact of the main determinants of GHG emission growth suggest that a differentiated application of European policies at country level will enhance the efficiency of mitigation efforts in Europe.

Climate change and pollution fighting have become prominent global concerns in the twenty-first century. In this context, accurate estimates for polluting emissions and their evolution are critical for robust policy-making processes and ultimately for solving stringent global climate challenges. As such, the primary objective of this study is to produce more accurate forecasts of greenhouse gas (GHG) emissions. This in turn contributes to the timely evaluation of the progress achieved towards meeting global climate goals set by international agendas and also acts as an early-warning system. We forecast the evolution of GHG emissions in 12 top polluting economies by using data for the 1970–2018 period and employing six econometric and machine-learning models (the exponential smoothing state-space model (ETS), the Holt–Winters model (HW), the TBATS model, the ARIMA model, the structural time series model (STS), and the neural network autoregression model (NNAR)), along with a naive model. A battery of robustness checks is performed. Results confirm a priori expectations and consistently indicate that the neural network autoregression model (NNAR) presents the best out-of-sample forecasting performance for GHG emissions at different forecasting horizons by reporting the lowest average RMSE (root mean square error) and MASE (mean absolute scaled error) within the array of predictive models. Predictions made by the NNAR model for the year 2030 indicate that total GHG emissions are projected to increase by 3.67% on average among the world’s 12 most polluting countries until 2030. Only four top polluters will record decreases in total GHG emissions values in the coming decades (i.e., Canada, the Russian Federation, the US, and China), although their emission levels will remain in the upper decile. Emission increases in a handful of developing economies will see significant growth rates (a 22.75% increase in GHG total emissions in Brazil, a 15.75% increase in Indonesia, and 7.45% in India) that are expected to offset the modest decreases in GHG emissions projected for the four countries. Our findings, therefore, suggest that the world’s top polluters cannot meet assumed pollution reduction targets in the form of NDCs under the Paris agreement. Results thus highlight the necessity for more impactful policies and measures to bring the set targets within reach.

Objective: The current study evaluated the greenhouse gas (GHG) emissions of dairy cattle through the Global Livestock Environmental Assessment Model (GLEAM) model and illustrated potential mitigation strategies by modifying nutrition interventions. Materials and Methods: A semi-structural questionnaire was developed to calculate dairy animal GHG emissions. This study comprised 40 farmers from four districts: river basin (Pabna), drought-prone (Chapainobabganj), floodplain (Nilphamari), and saline-prone (Sathkhira) areas. Ten lac¬tating cows (two cows from each farmer) were also selected to collect information on feeding practices, feed resources, feed intake (roughages and concentrate), water intake, and production and reproductive parameters for 7 days at each site during two seasons: dry (November– February) and wet (June–October). Results: The GHG emissions from the river basin area were significantly (p < 0.05) higher due to low-quality roughages (75%), whereas CH4/kg of milk production was the lowest (77.0 gm). In contrast, the area that frequently experiences drought showed a different pattern. For instance, the generation of CH4 from enteric fermentation was 1187.4 tons/year, while the production of CH4 and N2O from manure management was 323.1 tons/year and 4.86 tons/year, respectively. In comparison to other climatic areas, these values were the lowest because the supply of green grass was twice as abundant as in the other climatic areas (40%). The quantity of CH4/kg of milk produced in an area susceptible to drought did not vary. Conclusion: Implementing feeding systems in drought-prone areas is a successful approach to reducing GHG emissions in the dairy industry in Bangladesh. Consequently, implementing feed-balancing techniques can enhance productivity and foster environmentally sustainable ani¬mal production.

The synergistic greenhouse gas (GHG) emission reduction of the crop production (CP) and livestock farming (LF) sectors is of great significance for food security and low-carbon development, especially for China, the world leader in agricultural production. In this paper, the GHG emissions from the CP and LF sectors are accounted for and compared, and the spatial econometric model is adopted for comparative study based on the panel data from 1997 to 2021. The results show that: (1) The total amount and intensity of GHG emissions from both sectors showed obvious spatial heterogeneity and spatial dependence, and the spatial distribution pattern was relatively stable. (2) The influence of each factor on the GHG intensity and spatial characteristics of CP and LF varies widely. For the CP sector, economic development (local effect −0.29, adjacent effect +1.13), increased urbanization rate (−0.24, +0.16), agricultural structure (−0.29, +0.05), and urban-rural disparity (−0.03, +0.17) all reduce the GHG intensity of local region, while increasing the GHG intensity of its adjacent areas, signifying leakage. The economic structure (+0.06, +0.16), agricultural finance support (+0.02, +0.26), mechanization level (+0.05, +0.03), and land occupancy rate (+0.54, +0.44) all play a role in increasing the GHG intensity of CP in the local region and its adjacent areas. The disaster degree (−0.03, −0.03) also reduced the GHG intensity of CP. For the LF sector, economic structure (+0.08, +0.11), urban-rural disparity (+0.11, +0.21), agricultural development level (+0.03, +0.50), and increased land occupancy rate (+0.05, +0.01) can improve the GHG intensity of the one region and adjacent areas. Economic development (+0.03, −0.15), urbanization rate (+0.04, −0.30), agricultural structure (+0.09, −0.03), and disaster degree (+0.02, −0.06) can increase the GHG intensity of the local region while reducing the GHG intensity of adjacent areas. Based on the results, under the background of carbon peaking and carbon neutralization(dual-carbon) goals, this study first puts forward collaborative emission reduction measures for CP and LF, respectively, then further rises to sector synergy and regional synergy, and constructs the countermeasure system framework of collaborative emission reduction from three levels, to provide guidance and reference for the realization of dual goals of agricultural GHG reduction and food security.