As the world's second largest palm oil producer and exporter, Malaysia could capitalize on its oil palm biomass waste for power generation. The emission factors from this renewable energy source are far lower than that of fossil fuels. This study applies an integrated carbon accounting and mitigation (INCAM) model to calculate the amount of CO2 emissions from two biomass thermal power plants. The CO2 emissions released from biomass plants utilizing empty fruit bunch (EFB) and palm oil mill effluent (POME), as alternative fuels for powering steam and gas turbines, were determined using the INCAM model. Each section emitting CO2 in the power plant, known as the carbon accounting center (CAC), was measured for its carbon profile (CP) and carbon index (CI). The carbon performance indicator (CPI) included electricity, fuel and water consumption, solid waste and waste-water generation. The carbon emission index (CEI) and carbon emission profile (CEP), based on the total monthly carbon production, were determined across the CPI. Various innovative strategies resulted in a 20%-90% reduction of CO2 emissions. The implementation of reduction strategies significantly reduced the CO2 emission levels. Based on the model, utilization of EFB and POME in the facilities could significantly reduce the CO2 emissions and increase the potential for waste to energy initiatives.
Greenhouse gasses have adverse effects on global warming and air pollution and need to be optimized by minimizing the contributing factors. This work analyzes the effects of economic growth and energy resources (renewable and nonrenewable) on the emissions of greenhouse gasses (GHG). A 2000-2016 panel data from 25 developing Asian countries is analyzed through a robust Random Effect (RE) approach and Hausman Taylor Regression (HTR). Findings show a positive correlation between economic growth and energy consumption, while a 1% increase in renewable energy consumption results in a 0.193% decrease in carbon emissions. Economic growth and renewable energy are positively correlated in both the short and long term, which implies a valid feedback hypothesis. The findings indicate the significant contribution of nonrenewable energy resources to greenhouse gas emissions and the positive impact of renewable resources on greenhouse gas emissions' control. Furthermore, this study highlights the potential of developing Asian economies to preserve the environment through more robust regional environmental policies and renewable energy resources. In light of this study's findings, policymakers in Asian developing economies should develop policies on Renewable Energy infrastructure (RE) to improve GDP and reduce greenhouse gas emissions.
The well-established emissions-growth debate relies on the symmetric nexus between CO2 emissions and economic growth, thereby ignoring a fundamental component of macro economy in the form of asymmetric relation. This paper considers how CO2 emissions respond asymmetrically to changes in economic growth. While utilizing both linear and nonlinear time series approaches for an environmentally exposed country, Pakistan over the period 1971-2018, we find convincing evidence that CO2 emissions rise more rapidly during negative shocks to economic growth than increase during economic expansions. Thus, contrary to what has previously been reported, the effect is strong as holds both at short run and long run. This is partly due to the increase in informal sector as GDP declines. Our estimated results show that accounting for the shadow economy results a higher magnitude of CO2 emissions due to decrease in economic growth, thus question the traditional symmetric decoupling of economic growth and CO2 emissions. The estimated results are robust to alternative estimators such as fully modified least squares (FMOLS) and dynamic OLS (DOLS). Thus, the findings of this study call for a re-thinking on climate policy design that rarely pays attention to the aforementioned outcomes due to fall in economic growth.
This novel research is an argumentative subject which was needed to be addressed and to fill this gap, the author examined the effect of financial development, information and communication technology, and institutional quality on CO2 emission in Pakistan by using quantile autoregressive distributed lag (QARDL) model. The data were obtained for the period from 1995Q1 to 2018Q4. In the long run, GDP and institutional quality have a positive impact on CO2 emission when this emission is already high, which shows that if the GDP and institutional quality increases, the CO2 emission also increases. Moreover, financial development and ICT has a negative impact on CO2 emission irrespective of emission level that whether it is high or low in the country, which shows that if financial enhancement and ICT increases, carbon emission decreases. The study also supported the EKC hypothesis in Pakistan.
This study examines the association between transportation services (i.e., passenger and freight) and carbon emissions concerning the US economy. The monthly data for this study were collected for the period from 2000 M1 to 2019 M8. In this study, QARDL econometric approach as discussed by Cho et al. (2015) has been used to tests the relationship between transportation services and CO2 emissions. Due to the chaotic and nonlinear behavior of our concerning variables, it was quite difficult to gauge the principle properties of their variations. Therefore, we relied on QARDL, which has been missing in previous researches. By utilizing the QARDL method, this research assesses the long-term stability of the nexus across the quantiles to provide an econometric framework that is more flexible than the traditional ones. In particular, the authors have analyzed how the quantiles of transportation (i.e., passenger and freight) influence the quantiles of CO2 emissions (environmental degradation). The empirical evidence revealed the negative significant relationship of both the transportation system (i.e., passenger and freight) with carbon emissions; however, this relationship holds at low quantiles of freight transport, whereas the same relationship has been observed at the majority of quantiles of passenger transport. So, this depicts that the transportation system of the USA helps to reduce CO2 emissions. Therefore, to maintain this situation, the government shall introduce more technologies that are fuel-efficient and promote clean consumption, thus reducing CO2 emissions, boosting economic growth, and making green transportation services.
This paper uses the quantile autoregressive distributed lag (QARDL) model to analyze the impact of economic growth, tourism, transportation, and globalization on carbon dioxide (CO2) emissions in the Malaysian economy. The QARDL model is employed utilizing quarterly data from 1995Q1 to 2018Q4. The results demonstrate that economic growth is significantly positive with CO2 emissions at lower to upper quantiles. Interestingly, tourism has a negative effect on CO2 emissions at higher quantiles. Moreover, globalization and transportation services are positive, with CO2 emissions at upper-middle to higher quantiles. Furthermore, we tested the environmental Kuznets curve, and the outcomes confirm the presence of the inverted U-shaped curve in the Malaysian economy. The results of this study suggest that ecotourism is beneficial for economic growth in underdeveloped areas; it increases employment opportunities and, thus, achieves a win-win situation for protection and development. The government should encourage the low-carbon development of ecotourism and achieve green development of both tourism and the economy.
Being closely correlated with income and economic growth, trade openness impacts the environmental quality through different means. The study analyzes the robustness of the environmental Kuznets curve (EKC) hypothesis in OIC countries by examining the extent to which trade openness influence environmental quality through different environmental indicators for the period 1991 to 2018. A new methodology dynamic common correlated effects (DCCE) is applied to resolve the issue of cross-sectional dependence (CSD). We have used greenhouse gas (GHG) emissions, carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) along with ecological footprint as indicators of environmental quality. Results of DCCE estimation identify a negative association of trade openness with CO2, N2O, and CH4, while the positive relationship with the ecological footprint in overall OIC countries and higher income OIC countries. On the other hand, trade openness has a positive association with all environmental indicators in lower income OIC countries. Our findings confirm that inverted-U-shaped EKC exists in all groups of OIC countries when CO2, CH4, and ecological footprint are used as environmental indicators. However, a U-shaped EKC exists in overall OIC countries and lower income OIC countries when N2O is used. Eventually, it is recommended that if OIC countries continue trade openness policies and energy sector reforms and maintain sustainable use of biocapacity; then, they will be able to combat environmental issues with the increase in income.
Climate change caused by different anthropogenic activities is a subject of attention globally. There is a concern on how to maintain a clean environment and at the same time achieve optimal use of land. To this end, this study examines the causal effects of land use including agricultural, forestry, and other land categories on greenhouse gas (GHG) emissions. The data for China is collected over the period 1990 to 2012 for the empirical examination. By employing vector error correction model (VECM), it is found that there is significant long-run causality among variables. However, in the short run expectedly, only land under agriculture has strong causality with the GHG emissions. The results in case of variance decomposition analysis highlight that land under agriculture and other use significantly causes the GHG emissions in the long run. Further, impulse responses of variables are also measured with the Cholesky one standard deviation. The results are robust and support the argument that different land uses cause GHG emissions in China. The study provides insights for policy makers to improve the activities occurring on agricultural and other land uses. Assessment of overall potential, including bio energy, needs to include analysis of trade-offs and feedbacks with land-use competition. Many positive linkages with sustainable development and with adaptation exist but are case and site specific as they depend on scale, scope, and pace of implementation.
A study was carried out to assess carbon emission and carbon loss caused from land use change (LUC) of converting a wasteland into a Jatropha curcas plantation. The study was conducted for 12 months at a newly established Jatropha curcas plantation in Port Dickson, Malaysia. Assessments of soil carbon dioxide (CO(2)) flux, changes of soil total carbon and plant biomass loss and growth were made on the wasteland and on the established plantation to determine the effects of land preparation (i.e., tilling) and removal of the wasteland's native vegetation. Overall soil CO(2) flux showed no significant difference (P < 0.05) between the two plots while no significant changes (P < 0.05) on soil total carbon at both plots were detected. It took 1.5 years for the growth of Jatropha curcas to recover the biomass carbon stock lost during land conversion. As far as the present study is concerned, converting wasteland to Jatropha curcas showed no adverse effects on the loss of carbon from soil and biomass and did not exacerbate soil respiration.
Global warming is attracting attention from policy makers due to its impacts such as floods, extreme weather, increases in temperature by 0.7°C, heat waves, storms, etc. These disasters result in loss of human life and billions of dollars in property. Global warming is believed to be caused by the emissions of greenhouse gases due to human activities including the emissions of carbon dioxide (CO2) from petroleum consumption. Limitations of the previous methods of predicting CO2 emissions and lack of work on the prediction of the Organization of the Petroleum Exporting Countries (OPEC) CO2 emissions from petroleum consumption have motivated this research.
The objective of this paper is to examine the dynamic impact of urbanization, economic growth, energy consumption, and trade openness on CO 2 emissions in Nigeria based on autoregressive distributed lags (ARDL) approach for the period of 1971-2011. The result shows that variables were cointegrated as null hypothesis was rejected at 1 % level of significance. The coefficients of long-run result reveal that urbanization does not have any significant impact on CO 2 emissions in Nigeria, economic growth, and energy consumption has a positive and significant impact on CO 2 emissions. However, trade openness has negative and significant impact on CO 2 emissions. Consumption of energy is among the main determinant of CO 2 emissions which is directly linked to the level of income. Despite the high level of urbanization in the country, consumption of energy still remains low due to lower income of the majority populace and this might be among the reasons why urbanization does not influence emissions of CO 2 in the country. Initiating more open economy policies will be welcoming in the Nigerian economy as the openness leads to the reduction of pollutants from the environment particularly CO 2 emissions which is the major gases that deteriorate physical environment.
Waste collection is an important part of waste management that involves different issues, including environmental, economic, and social, among others. Waste collection optimization can reduce the waste collection budget and environmental emissions by reducing the collection route distance. This paper presents a modified Backtracking Search Algorithm (BSA) in capacitated vehicle routing problem (CVRP) models with the smart bin concept to find the best optimized waste collection route solutions. The objective function minimizes the sum of the waste collection route distances. The study introduces the concept of the threshold waste level (TWL) of waste bins to reduce the number of bins to be emptied by finding an optimal range, thus minimizing the distance. A scheduling model is also introduced to compare the feasibility of the proposed model with that of the conventional collection system in terms of travel distance, collected waste, fuel consumption, fuel cost, efficiency and CO2 emission. The optimal TWL was found to be between 70% and 75% of the fill level of waste collection nodes and had the maximum tightness value for different problem cases. The obtained results for four days show a 36.80% distance reduction for 91.40% of the total waste collection, which eventually increases the average waste collection efficiency by 36.78% and reduces the fuel consumption, fuel cost and CO2 emission by 50%, 47.77% and 44.68%, respectively. Thus, the proposed optimization model can be considered a viable tool for optimizing waste collection routes to reduce economic costs and environmental impacts.
Validity of the environmental Kuznets curve (EKC) hypothesis is consistently and widely debated among economists and environmentalists alike throughout time. In Malaysia, transport is one of the "dirtiest" sectors; it intensively consumes energy in powering engines by using fossil fuels and poses significant threats to environmental quality. Therefore, this study attempted an examination into the impact of corruption on transport carbon dioxide (CO2) emissions. By adopting the fully modified ordinary least squares, canonical cointegrating regression, and dynamic ordinary least squares in performing long-run estimations, the results obtained based on the annual data spanning from 1990 to 2017 yielded various notable findings. First, more corruption would be attributable towards increased transport CO2 emissions. Second, a monotonic increment of transport CO2 emission was seen with higher economic growth and thus invalidated the presence of EKC. Overall, this study suggests that Malaysia has yet to reach the level of economic growth synonymous with transport CO2 emission reduction due to the lack of high technology usage in the current system implemented. Therefore, this study could position policy recommendations of use to the Malaysian authorities in designing the appropriate economic and environmental policies, particularly for the transport sector.
This paper assesses the Environmental Kuznets curve based on quantile behavior of the relationship between economic growth, forest area, agriculture production, renewable energy, and environmental degradation. The current literature generally used a single indicator to address environmental issues; however single indicator neither measures overall environmental conditions nor does specify that the environment issue is generally diminishing. Our study is the first one that used ecological footprint (EF) as an indicator to test environmental Kuznets curve (EKC) hypothesis for Pakistan by employing recent approach of quantile autoregressive distributed lag (QARDL) initiated by Cho et al. (J Econ 188(1):281-300, 2015). The result of this study validates the EKC hypothesis for Pakistan and shows quantile-dependent relationship, and in that case, using the conventional methods may somewhat lead to biased results. Moreover, the rejection of the null hypothesis of parameter constancy is also confirmed by Wald test. In the long run, the findings of renewable energy consumption and forest area show significant negative effects on ecological footprints, which indicates that by increasing renewable energy usage and forest area, ecological footprints can be minimized. Interestingly, the short-term effects of agricultural production findings on EF show statistically negative results. This illustrates that EF can also be reduced in the agriculture sector by adopting environment-friendly technologies. In order to create efficient policies for environment deterioration, the empirical findings of the current analysis can be used as a guideline for policy implications.
The aim of this research is to explore the association between financial development, research and development (R&D) expenditures, globalization, institutional quality, and energy consumption in India by using the quarterly data of 1995-2018. Quantile Autoregressive Distributed Lag (QARDL) approach is employed to examine the relationship. An application of the QARDL approach suggests that the R&D, financial development, globalization, and institutional quality significantly influence energy utilization in India. R&D and institutional quality have a negative effect on energy utilization which shows that due to the increase in the quality of institutions and R&D in the country, energy utilization is likely to decrease. However, globalization and financial performance have a positive influence on energy which depicts that due to the increase in financial performance and globalization in India the energy consumption is likely to increase. According to the outcomes of this research, India should make a policy to ease the penalties of energy utilization by monitoring resource transfer by means of globalization and by implementing energy conversation procedures through the advancement of the financial sector.
This study is a scholarly effort to broaden the existing literature on the impact of transportation services, urbanization, and financial development on ecological footprints in Pakistan. Data used in this study covers the period of 39 years from 1980 to 2018. This study adopted the QARDL model to tackle the non-linear association of variables and test their long-run stability across the different quantiles. The findings of this study indicated a significant negative association of transportation services and financial development with ecological footprints in Pakistan at almost all quantiles whereas, the urban population was found to be positively associated with the ecological footprint in Pakistan. Results also justify the existence of the EKC hypothesis in the scenario of Pakistan. Policymakers are advised to frame strategies for investors to invest more in eco-friendly projects to curtail the ecological footprints in Pakistan. Minimizing the dependency of the transportation sector on fossil fuel, and increased use of energy-efficient appliances in the urban population would be beneficial to control the negative influence on ecological footprints in Pakistan.
The main objective of this paper is to estimate the interfuel substitution elasticities between hydropower and the fossil fuels of coal and natural gas used in the generation of electricity for Malaysia. Due to the violation of the assumption behind the ordinary least squares (OLS) method on account of the correlated error terms in the system of equations, the econometrics techniques of seemingly unrelated regression (SUR) was adopted to obtain the parameter estimates using dataset that covers the period 1988 to 2016. The main finding is that there exists substantial substitution possibility between hydropower and fossil fuels in the generation of electricity for Malaysia. CO2 emissions mitigation scenarios were also conducted to explore the possible effects of substituting fossil fuels for hydropower to generate electricity. The results show that switching from high carbon-emitting fuels to renewable energy such as hydropower will substantially reduce CO2 emission and assist the country towards achieving the carbon emissions reduction targets. Policy recommendations are offered in the body of the manuscript.
Tropical peatlands are vital ecosystems that play an important role in global carbon storage and cycles. Current estimates of greenhouse gases from these peatlands are uncertain as emissions vary with environmental conditions. This study provides the first comprehensive analysis of managed and natural tropical peatland GHG fluxes: heterotrophic (i.e. soil respiration without roots), total CO2 respiration rates, CH4 and N2 O fluxes. The study documents studies that measure GHG fluxes from the soil (n = 372) from various land uses, groundwater levels and environmental conditions. We found that total soil respiration was larger in managed peat ecosystems (median = 52.3 Mg CO2 ha-1 year-1 ) than in natural forest (median = 35.9 Mg CO2 ha-1 year-1 ). Groundwater level had a stronger effect on soil CO2 emission than land use. Every 100 mm drop of groundwater level caused an increase of 5.1 and 3.7 Mg CO2 ha-1 year-1 for plantation and cropping land use, respectively. Where groundwater is deep (≥0.5 m), heterotrophic respiration constituted 84% of the total emissions. N2 O emissions were significantly larger at deeper groundwater levels, where every drop in 100 mm of groundwater level resulted in an exponential emission increase (exp(0.7) kg N ha-1 year-1 ). Deeper groundwater levels induced high N2 O emissions, which constitute about 15% of total GHG emissions. CH4 emissions were large where groundwater is shallow; however, they were substantially smaller than other GHG emissions. When compared to temperate and boreal peatland soils, tropical peatlands had, on average, double the CO2 emissions. Surprisingly, the CO2 emission rates in tropical peatlands were in the same magnitude as tropical mineral soils. This comprehensive analysis provides a great understanding of the GHG dynamics within tropical peat soils that can be used as a guide for policymakers to create suitable programmes to manage the sustainability of peatlands effectively.
Greenhouse gases (GHGs) carbon dioxide (CO2) and nitrous oxide (N2O), contribute significantly to global warming, and they have increased substantially over the years. Reforestation is considered as an important forestry application for carbon sequestration and GHGs emission reduction, however, it remains unknown whether reforestation may instead produce too much CO2 and N2O contibuting to GHGs pollution. This study was performed to characterize and examine the CO2 and N2O emissions and their controlling factors in different species and types of pure and mixture forest used for reforestation. Five soil layers from pure forest Platycladus orientalis (PO), Robinia pseudoacacia (RP), and their mixed forest P-R in the Taihang mountains of central China were sampled and incubated aerobically for 11 days. The P-R soil showed lower CO2 and N2O production potentials than those of the PO soils (P
The increasing level of greenhouse gas carbon emission currently exacerbates the devastating effect of global warming on the Earth's ecosystem. Energy usage is one of the most important determinants that is increasing the amount of carbon gases being released. Simultaneously, the level of energy usage is derived by the price, and therefore, this study examines the contribution of energy price to carbon gas emissions in thirteen African nations for the period spanning 1990 to 2017. It does this by utilising the cross-sectional dependence (CD), augmented mean group (AMG) and pooled mean group (PMG) panel modelling methods. The findings of the AMG model suggest that a 1% increase in energy price leads to a 0.02% decrease in carbon emission. The results further reveal that a 1% increase in energy intensity and technological innovation leads to 0.04% and 3.65% increase in carbon emission, respectively, in the selected African countries. Findings will help policymakers to implement effective energy price policies to reduce carbon emissions and achieve sustainable development goals especially in the emerging economies of Africa.