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.
The aim of this research is to explore the effect of biomass energy consumption on CO2 emissions in 80 developed and developing countries. To achieve robustness, the system generalised method of moment was used and several control variables were incorporated into the model including real GDP, fossil fuel consumption, hydroelectricity production, urbanisation, population, foreign direct investment, financial development, institutional quality and the Kyoto protocol. Relying on the classification of the World Bank, the countries were categorised to developed and developing countries. We also used a dynamic common correlated effects estimator. The results consistently show that biomass energy as well as fossil fuel consumption generate more CO2 emissions. A closer look at the results show that a 100% increase in biomass consumption (tonnes per capita) will increase CO2 emissions (metric tons per capita) within the range of 2 to 47%. An increase of biomass energy intensity (biomass consumption in tonnes divided by real gross domestic product) of 100% will increase CO2 emissions (metric tons per capita) within the range of 4 to 47%. An increase of fossil fuel consumption (tonnes of oil equivalent per capita) by 100% will increase CO2 emissions (metric tons per capita) within the range of 35 to 55%. The results further show that real GDP urbanisation and population increase CO2 emissions. However, hydroelectricity and institutional quality decrease CO2 emissions. It is further observed that financial development, foreign direct investment and openness decrease CO2 emissions in the developed countries, but the opposite results are found for the developing nations. The results also show that the Kyoto Protocol reduces emission and that Environmental Kuznets Curve exists. Among the policy implications of the foregoing results is the necessity of substituting fossil fuels with other types of renewable energy (such as hydropower) rather than biomass energy for reduction of emission to be achieved.
This study empirically investigates the effect of meat consumption on greenhouse gas emissions (carbon dioxide, methane, and nitrous oxide) in the USA. The impact of meat consumption on greenhouse gas emissions is examined by controlling for economic growth and energy consumption. The empirical analysis finds that all these variables are cointegrated for the long run. Moreover, meat consumption aggravates greenhouse gas emissions. Specifically, meat consumption (except for beef) has a U-shaped relationship with carbon emissions and an inverted U-shaped relationship with methane and nitrous oxide emissions. The causality analysis indicates a unidirectional causality running from meat consumption to greenhouse gas emissions. These empirical findings indicate that the US livestock sector has the potential to become more environmentally friendly with careful policy formulation and implementation.
The determinants of environmental degradation have been investigated many times by utilizing carbon dioxide emissions and/or ecological footprint. However, these traditional environmental degradation indicators do not consider the supply side of environmental problems. Therefore, this study focuses on the dynamic influence of financial development, energy efficiency, economic growth, and technological innovation on environmental degradation in India through the load capacity factor, including both the supply and demand sides of environmental problems. For that purpose, the recently developed dynamically simulated autoregressive distributed lag (ARDL) method is employed using the annual time-series data extending from 1980-2020. The dynamically simulated ARDL results demonstrate that financial development, economic growth, and technological innovation have a dynamic adverse impact on the load capacity factor, whereas energy efficiency has a positive dynamic influence on environmental quality. In addition, the results support the validity of the environmental Kuznets curve hypothesis as the negative effect of economic growth on environmental quality decreases over time. Based on the study findings, policy recommendations are provided for India. Finally, this study utilizing load capacity factor as an indicator for environmental quality will provide new topics in exploring the determinants of environmental degradation.
The present paper implements the quantile autoregressive lagged (QARDL) approach of Cho et al. (2015) and the Granger causality in quantiles tests of Troster et al. (2018) to explore the nonlinear effects of US energy consumption, economic growth, and tourist arrivals on carbon dioxide (CO2) emission. Our results unveil the existence of substantial reversion to the long-run equilibrium connectedness between the variables of interest and CO2 emissions. The outcomes show that tourist arrivals decrease CO2 emissions in the long term for each quantile. In addition, we found that the output growth positively influences the carbon emissions at lower quantiles but negatively influences the carbon emissions at upper quantiles. Moreover, our findings of short-term dynamics validate an asymmetric short-run effect of tourist arrivals and economic growth on CO2 emissions in the US economy. Further results and their corresponding policy implications are discussed.
Energy is one of the prime factors in influencing the sustainable development of a country. Different energy sources play important roles in driving the income growth of different economic sectors such as industrial, agricultural, and services. Fossil fuels, however, have come under strong criticism for actively accelerating climate change. As such, it is imperative to investigate the contributions of various energy sources toward sustainable growth. With Malaysia as the test-bed, the present study analyzes the impact of energy prices on economic stability using the novel wavelet-based analysis. Specifically, the study analyzed the impact of crude oil, natural gas, and gasoline prices on the economic (brown) and green growth from 1995 to 2020. The results show that in continuous wavelet transform, the cone of influence of all five factors exhibits strong short-run variance and fluctuations from 2005 to 2013. However, the intensity of brown growth is more influential than green growth. Similarly, in wavelet coherence graphs, the downward right arrows indicate positively significant associations between crude oil prices, natural gas prices, and gasoline prices with brown and green growth. Additionally, wavelet-based Granger causality reveals a bidirectional causal relationship between all variables. The results thus strongly suggest that energy prices predominantly affect the economic (brown) and green growth progression of the Malaysian economy. The study concludes with some suggested implications to augment the country's sustainable growth.
Carbon dioxide (CO2) emission will increase due to the increasing global plastic demand. Statistical data shows that plastic production alone will contribute to at least 20% of the annual global carbon budget in the near future. Hence, several alternative methods are recommended to overcome this problem, such as bio-product synthesis. Algae consist of diverse species and have huge potential to be a promising biomass feedstock for a range of purposes, including bio-oil production. The convenient cultivation method of algae could be one of the main support for algal biomass utilization. The aim of this study is to forecast and outline the strategies in order to meet the future demand (year 2050) of plastic production and, at the same time, reduce CO2 emission by replacing the conventional plastic with bio-based plastic. In this paper, the analysis for 25%, 50% and 75% CO2 reduction has been done by using carbon emission pinch analysis. The strategies of biomass utilization in Malaysia are also enumerated in this study. This study suggested that the algal biomass found in Malaysia coastal areas should be utilized and cultivated on a larger scale in order to meet the increasing plastic demand and, at the same time, reduce carbon footprint. Some of the potential areas for macroalgae sea-farming cultivation in Sabah coastline (Malaysia), comprised of about 3885 km2 (388,500 ha) in total, have been highlighted. These potential areas have the potential to produce up to 14.5 million tonnes (Mt)/y of macroalgae in total, which can contribute 370 Mt of phenol for bioplastic production.
The rise of urbanisation in Belt and Road Initiative (BRI) countries that contribute to the disruption of the ecosystem, which would affect global sustainability, is a pressing concern. This study provides new evidence of the impact of urbanisation and institutional quality on greenhouse gas (GHG) emissions in the selected 48 BRI countries from the years 1984 to 2017. The models of this study are inferred by using panel regression model and panel quantile regression model to meet the objectives of our study as it contemplates unobserved country heterogeneity. From the panel regression model, the findings indicate that although urbanisation in BRI supports the 'life effect' hypothesis that could dampen the environment quality, this effect could be reduced through better institutional quality. Using the quantile regression method, this study concludes that one-size-fits-all strategies to reduce GHG emissions in countries with different GHG emissions levels are improbable to achieve success for all. Hence, GHG emissions control procedures should be adjusted differently across high-emission, middle-emission and low-emission countries. Based on these results, this study provides novel intuitions for policymakers to wisely plan the urbanisation blueprints to eradicate unplanned urbanisation and improve institutional quality in meeting pollution mitigation goals.
Conversion of tropical peat swamp forest to drainage-based agriculture alters greenhouse gas (GHG) production, but the magnitude of these changes remains highly uncertain. Current emissions factors for oil palm grown on drained peat do not account for temporal variation over the plantation cycle and only consider CO2 emissions. Here, we present direct measurements of GHGs emitted during the conversion from peat swamp forest to oil palm plantation, accounting for CH4 and N2O as well as CO2. Our results demonstrate that emissions factors for converted peat swamp forest is in the range 70-117 t CO2 eq ha-1 yr-1 (95% confidence interval, CI), with CO2 and N2O responsible for ca. 60 and ca. 40% of this value, respectively. These GHG emissions suggest that conversion of Southeast Asian peat swamp forest is contributing between 16.6 and 27.9% (95% CI) of combined total national GHG emissions from Malaysia and Indonesia or 0.44 and 0.74% (95% CI) of annual global emissions.
Tropical peatlands are globally important ecosystems with high C storage and are endangered by anthropogenic disturbances. Microbes in peatlands play an important role in sustaining the functions of peatlands as a C sink, yet their characteristics in these habitats are poorly understood. This research aimed to elucidate the responses of these complex ecosystems to disturbance by exploring greenhouse gas (GHG) emissions, nutrient contents, soil microbial communities and the functional interactions between these components in a primary and secondary peat swamp forest in Peninsular Malaysia. GHG measurements using closed chambers, and peat sampling were carried out in both wet and dry seasons. Microbial community phenotypes and nutrient content were determined using phospholipid fatty acid (PLFA) and inductively-coupled plasma mass spectrometry (ICP-MS) analyses respectively. CO2 emissions in the secondary peat swamp forest were > 50% higher than in the primary forest. CH4 emission rates were ca. 2 mg m-2 h-1 in the primary forest but the secondary forest was a CH4 sink, showing no seasonal variations in GHG emissions. Almost all the nutrient concentrations were significantly lower in the secondary forest, postulated to be due to nutrient leaching via drainage and higher rates of decomposition. Cu and Mo concentrations were negatively correlated with CO2 and CH4 emissions respectively. Microbial community structure was overwhelmingly dominated by bacteria in both forest types, however it was highly sensitive to land-use change and season. Gram-positive and Gram-negative relative abundance were positively correlated with CO2 and CH4 emissions respectively. Drainage related disturbances increased CO2 emissions, by reducing the nutrient content including some with known antimicrobial properties (Cu & Na) and by favouring Gram-positive bacteria over Gram-negative bacteria. These results suggest that the biogeochemistry of secondary peat swamp forest is fundamentally different from that of primary peat swamp forest, and these differences have significant functional impacts on their respective environments.
The aim of this study was to optimize the antioxidant activity of Piper nigrum L. essential oil extracted using the supercritical carbon dioxide (SC-CO₂) technique. Response surface methodology was applied using a three-factor central composite design to evaluate the effects of three independent extraction variables: pressure of 15-30 MPa, temperature of 40-50 °C and dynamic extraction time of 40-80 min. The DPPH radical scavenging method was used to evaluate the antioxidant activity of the extracts. The results showed that the best antioxidant activity was achieved at 30 MPa, 40 °C and 40 min. The extracts were analyzed by GC-FID and GC-MS. The main components extracted using SC-CO₂ extraction in optimum conditions were β-caryophyllene (25.38 ± 0.62%), limonene (15.64 ± 0.15%), sabinene (13.63 ± 0.21%), 3-carene (9.34 ± 0.04%), β-pinene (7.27 ± 0.05%), and α-pinene (4.25 ± 0.06%). The essential oil obtained through this technique was compared with the essential oil obtained using hydro-distillation. For the essential oil obtained by hydro-distillation, the most abundant compounds were β-caryophyllene (18.64 ± 0.84%), limonene (14.95 ± 0.13%), sabinene (13.19 ± 0.17%), 3-carene (8.56 ± 0.11%), β-pinene (9.71 ± 0.12%), and α-pinene (7.96 ± 0.14%). Radical scavenging activity of the extracts obtained by SC-CO₂ and hydro-distillation showed an EC₅₀ of 103.28 and 316.27 µg mL(-1) respectively.
This paper investigates the non-linear impacts of the agricultural, industrial, financial, and service sectors on environmental pollution in Malaysia during the 1980-2018 period. It employs the extended STIRPAT model and two indicators of environmental pollution (carbon dioxide emissions and ecological footprints). It uses the autoregressive distributed lag (ARDL) technique to estimate the parameters. Evidence from the study indicate that the agricultural, industrial, and service sectors have inverted U-shaped non-linear impacts on carbon dioxide emissions and ecological footprints, while the financial sector has a U-shaped non-linear relationship with carbon dioxide emissions and ecological footprint. These empirical outcomes are robust to diagnostic tests, structural breaks, and alternative estimation technique and proxies. The economic implication of this paper is that, at the early stage of sectoral growth, the pollution intensity of sectoral output increases, but after a certain turning point, a further increase in sectoral output will reduce environmental pollution. Precisely, environmental pollution will reduce if the agricultural, industrial, and service sectors exceed threshold levels of 11%, 44%, and 49% of GDP, respectively, while environmental pollution will be aggravated if financial sector exceeds a threshold level of 94%. Therefore, efforts to mitigate environmental pollution in Malaysia should integrate sectoral growth to attain sustainable development.
In this era of globalization, various products and technologies are being developed by the industries. While resources and energy are utilized from processes, wastes are being excreted through water streams, air, and ground. Without realizing it, environmental pollutions increase as the country develops. Effective technology is desired to create green factories that are able to overcome these issues. Wastewater is classified as the water coming from domestic or industrial sources. Wastewater treatment includes physical, chemical, and biological treatment processes. Aerobic and anaerobic processes are utilized in biological treatment approach. However, the current biological approaches emit greenhouse gases (GHGs), methane, and carbon dioxide that contribute to global warming. Microalgae can be the alternative to treating wastewater as it is able to consume nutrients from wastewater loading and fix CO2 as it undergoes photosynthesis. The utilization of microalgae in the system will directly reduce GHG emissions with low operating cost within a short period of time. The aim of this review is to discuss the uses of native microalgae species in palm oil mill effluent (POME) and flue gas remediation. In addition, the discussion on the optimal microalgae cultivation parameter selection is included as this is significant for effective microalgae-based treatment operations.
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.
The medium-chain-length polyhydroxyalkanoate (PHA(MCL)) produced by Pseudomonas putida PGA1 using saponified palm kernel oil as the carbon source could degrade readily in water taken from Kayu Ara River in Selangor, Malaysia. A weight loss of 71.3% of the PHA film occurred in 86 d. The pH of the river water medium fell from 7.5 (at d 0) to 4.7 (at d 86), and there was a net release of CO2. In sterilized river water, the PHA film also lost weight and the pH of the water fell, but to lesser extents. The C8 monomer of the PHA was completely removed after 6 d of immersion in the river water, while the proportions of the other monomers (C10, C12, and C14) were reversed from that of the undegraded PHA. By contrast, the monomer composition of the PHA immersed in sterilized river water did not change significantly from that of the undegraded PHA. Scanning electron microscopy showed physical signs of degradation on the PHA film immersed in the river water, but the film immersed in sterilized river water was relatively unblemished. The results thus indicate that the PHA(MCL) was degraded in tropical river water by biologic as well as nonbiologic means. A significant finding is that shorter-chain monomers were selectively removed throughout the entire PHA molecule, and this suggests enzymatic action.
This study examines the impact of energy consumption, urbanization, and globalization on environmental degradation proxied by carbon emissions (CO2) in the South Asian Association for Regional Cooperation (SAARC) countries, namely Sri Lanka, Pakistan, Maldives, Nepal, Bhutan, Bangladesh, and India using data over the period 1990-2018. The cross-sectional autoregressive distributed lag (CS-ARDL), pooled mean group (PMG), and Dumitrescu and Hurlin (D-H) Granger causality techniques are employed for the empirical analysis. First and second-generation panel unit root tests are used to determine the stationary level of all data series which reveals mixed order of integration. The empirical findings show that urbanization, gross domestic product (GDP) per capita income, energy consumption, industrial growth, globalization, and financial development cause CO2 emissions, while the other variables, namely arable land and innovation, put negative effects on CO2 emissions. Moreover, the D-H heterogeneous test results exhibit that bi-directional relationship exists between CO2 and arable land, urbanization, industrial growth, and financial development, while a unidirectional causality exists between CO2 emissions and GDP per head income. These findings suggest that planned urbanization, investment in renewable energy sources, and effective strategies regarding the economic and financial integration with the global economies are required for a clean and green environment.
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.
Information on temporal and spatial variations in soil greenhouse gas (GHG) fluxes from tropical peat forests is essential to predict the influence of climate change and estimate the effects of land use on global warming and the carbon (C) cycle. To obtain such basic information, soil carbon dioxide (CO2) and methane (CH4) fluxes, together with soil physicochemical properties and environmental variables, were measured at three major forest types in the Maludam National Park, Sarawak, Malaysia, for eight years, and their relationships were analyzed. Annual soil CO2 fluxes ranged from 860 to 1450 g C m⁻2 yr⁻1 without overall significant differences between the three forest sites, while soil CH4 fluxes, 1.2-10.8 g C m⁻2 yr⁻1, differed. Differences in GHG fluxes between dry and rainy seasons were not necessarily significant, corresponding to the extent of seasonal variation in groundwater level (GWL). The lack of significant differences in soil CO2 fluxes between the three sites could be attributed to set-off between the negative and positive effects of the decomposability of soil organic matter as estimated by pyrophosphate solubility index (PSI) and GWL. The impact of El-Niño on annual CO2 flux also varied between the sites. The variation in soil CH4 fluxes from the three sites was enhanced by variations in temperature, GWL, PSI, and soil iron (Fe) content. A positive correlation was observed between the annual CH4 flux and GWL at only one site, and the influence of soil properties was more pronounced at the site with the lowest GWL and the highest PSI. Variation in annual CH4 fluxes was controlled more strongly by temperature where GWL was the highest and GWL and plant growth fluctuations were the least. Inter-annual variations in soil CO2 and CH4 fluxes confirmed the importance of long-term monitoring of these at multiple sites supporting different forest types.
In light of a slow buildup in CO2 emissions since the recovery, this paper revisits the relationship between CO2 emissions and the US economy using a nonlinear autoregressive distributed lag model, in which the determinants are identified through an expanded real business cycle model. We find convincing evidence that CO2 emissions decline more rapidly during recessions than increase during expansions over the long run. Of all determinants considered, long-run asymmetry is fostered once vehicle miles traveled is controlled. This calls for a greater attention to public transportation development and vehicle miles traveled tax for slowing down stock buildup of CO2 emissions during good times.