BAU Scenario Research Articles

Grassland or Cropland? Land Use Dilemma and Ecological Solutions in Inner Mongolia

ABSTRACTInner Mongolia plays a critical role in both ecological conservation and food provision in China. However, some researchers have argued that focusing on and improving only one side of the equation necessarily threatens the functionality of the opposite side. To address this problem, we compared a “business‐as‐usual” scenario (BAU) with a “sustainable land use planning” scenario (SLU) constructed by simulating spatiotemporal changes in croplands and grasslands in Inner Mongolia from 2020 to 2030. Additionally, we analyzed the changes in ecosystem services and protein supply associated with changes in land use. We found that, in the BAU scenario, grasslands would decrease by 1.85% over the simulation period, while croplands would increase by 9.94%, with ecosystem services decreasing under both land uses. In contrast, land use changes over the same period in the SLU scenario are more significant, with increases of 11.33% and 2.78% in grassland and cropland, respectively, but, in this case, with ecosystem services increasing under both land uses. Moreover, protein supply increased under both scenarios, but SLU scenario can provide 33% more protein than the BAU scenario. The interconversion of cropland and grassland is the main type of land conversion in the study region, while cropland, grassland, and bare land show a triangular cycle of conversion. In addition, the implementation of scenario planning can realize multiple dividend for cultivation, livestock, and ecology in Inner Mongolia.

Carbon emissions reduction path of logistics industry in Hebei, China: based on internal factors

To explore the optimal carbon emissions reduction path for the logistics industry in Hebei Province, China, this paper considers internal factors such as logistics agglomeration, transportation structure, and technology innovation as regulatory measures. The system dynamics method is employed to simulate the effects of carbon emissions reduction for each factor. Then, based on examining the interactive relationships among these internal factors, the paper proposes the optimal carbon emissions reduction path for Hebei Province’s logistics industry under different sequences of implementing the three measures. The results indicate that logistics agglomeration, transportation structure, and technological innovation can contribute to varying degrees of carbon emissions reduction in logistics. By 2030, it is projected that logistics agglomeration will result in a reduction of 21,799 million tons of carbon emissions while transportation structure and technology innovation will lead to reductions of 23,459 million tons and 20,778 million tons respectively compared to the BAU scenario. Furthermore, intricate interactions exist among these three internal measures. The optimal path for reducing carbon emissions is TSI-AGD-LTI. Specifically, to optimize the transportation structure first, relieve the pressure of logistics carbon emissions aggravated by logistics agglomeration, promote the cross-regional transfer of logistics carbon emissions, and then improve the level of technology innovation to strengthen further the logistics carbon emissions reduction effect of transportation structure adjustment.

Study on the Pathway of Energy Transition in Inner Mongolia under the "Dual Carbon" Goal

As an important strategic energy base in China, Inner Mongolia's energy exports are dominated by coal and electricity. Under the background of "double carbon" target, the energy transition of Inner Mongolia is of great significance to China’s energy security and carbon emission reduction. Based on the energy policy simulation model (EPS model), this paper explores the path of energy transition in Inner Mongolia by constructing the scenarios of developing renewable energy, developing CCS technology and carbon pricing, and simulating the policy situation based on the reality of Inner Mongolia and the energy transition experience of developed countries and regions. The results show that (1) The energy production and power generation under the development of renewable energy scenario (RE) and carbon pricing scenario (CP) are lower than the "Business as usual" scenario (BAU) for a certain period of time, which affects China's energy security. However, by 2060, power generation and energy production under both scenarios exceed the BAU scenario, and the energy production structure is dominated by renewable energy in both cases. (2) If the Inner Mongolia government focuses on short-term carbon emission reduction, the government should actively promote the establishment of a carbon emission trading market and increase the use of renewable energy through the improvement of the carbon pricing mechanism to promote carbon emission reduction in Inner Mongolia. If the Inner Mongolia government focuses on long-term energy supply stability, it should vigorously develop CCS technology.

Land Use Simulation and Ecological Network Construction around Poyang Lake Area in China under the Goal of Sustainable Development

The pivotal aspects of enhancing regional ecosystem services and augmenting socioeconomic growth lie in optimizing the land-space development and protection strategies, coupled with the establishment of a robust ecological network (EN). This article examines the Poyang Lake area and employs the MOP model, NSGA-II, and PLUS model to determine the best sustainable land use strategy. Subsequently, the MSPA, InVEST model, circuit theory, complex network, and others are employed to construct and analyze the land-space EN across three time periods. Ultimately, the EN is optimized based on spatial protection priority, ecological obstacle areas, and ecological nodes. The results show the following: (1) From 2005 to 2035, more construction land will be developed around the Greater Nanchang area and other urban centers. In the BAU scenario, construction land will expand faster, while cultivated land, forest, grassland, and bare land will continue to decline. In the SD scenario, the alteration to comparable land is minimal, the growth rate of construction land will slow, cultivated land, forest, grassland, and bare land will all decline little, and the water area will increase slightly; (2) While the area of ecological sources is decreased and ecological corridors become longer and narrower in the BAU scenario, the spatial distribution of ENs in different periods is small, and the quantitative structure and spatial distribution of ecological sources and corridors are essentially unchanged in the SD scenario; (3) Based on the topological structure of ENs, it is found that the clustering of nodes in the SD scenario is more obvious, the importance of ecological sources is enhanced, the efficiency of information transmission is improved, and the radiation range is wider and more stable; (4) The greatest priority ecological sources in each period are concentrated around Poyang Lake. In the SD scenario, the priority of ecological sources improves, and 7025 km2 of ecological obstacle restoration area is identified, with 41, 31, and 36 ecological breakpoints in the first, second, and third levels. The study’s findings can assist and shape theoretical and practical approaches to land governance and sustainable development in great lake areas.

Adaptive forest management improves stand-level resilience of temperate forests under multiple stressors

Forests are expected to be strongly affected by modifications in climate and disturbance regimes, threatening their ability to sustain the provision of essential services. Promoting drought-tolerant species or functionally diverse stands have recently emerged as management options to cope with global change. Our study aimed at evaluating the impact of contrasting stand-level management scenarios on the resilience of temperate forests in eastern North America and central-western Europe using the individual process-based model HETEROFOR. We simulated the evolution of eight stands over 100 years under a future extreme climate according to four management scenarios (business as usual - BAU; climate change adaptation - CC; functional diversity approach - FD; no management - NM) while facing multiple disturbances, resulting in a total of 160 simulations. We found that FD demonstrated the greatest resilience regarding transpiration and tree biomass, followed by CC and then BAU, while these three scenarios were equivalent concerning the net primary production. These results were however dependent on forest type: increasing functional diversity was a powerful option to increase the resilience of coniferous plantations whereas no clear differences between BAU and adaptive management scenarios were detected in broadleaved and mixed stands. The FD promoted a higher level of tree species diversity than any other scenario, and all scenarios of management were similar regarding the amount of harvested wood. The NM always showed the lowest resilience, demonstrating that forest management could be an important tool to mitigate adverse effects of global change. Our study highlighted that tree-level process-based models are a relevant tool to identify suitable management options for adapting forests to global change provided that model limitations are considered, and that alternative management options, particularly those based on functional diversity, are promising and should be promoted from now on.

Maps of Soil Organic Carbon Sequestration Potential in the Russian Croplands

Adoption of the farming systems that aim to sequester carbon in agricultural soils is one of the ways to mitigate global climate change. This study focuses on the estimation of organic carbon sequestration potential of the Russian croplands in the upper (0–30 cm) soil layer by creating a set of maps using the data from global and national databases as the input data. The maps are generated within the FAO Global Soil Organic Carbon Sequestration Potential Map (GSOCseq) project according to the unified methodology using the RothC model to predict the rate of carbon sequestration in 2020–2040 under a business as usual scenario (BAU), as well as under sustainable soil management scenarios with additional different C input (+5, +10, and +20%) resulting from the use of sustainable soil management (SSM) scenarios. The total potential sequestration rate by the croplands of the Russian Federation in the 0–30-cm layer under a BAU scenario is assessed at 8.5 Mt/year and the estimate under SSM scenarios, up to 25.5 Mt/year. The carbon sequestration by the cropland of each soil ecological zone (except for the light chestnut (Eutric Cambisols (Protocalcic)) and brown semidesert (Luvic Calcisols) soils, where it is around zero) and on a national scale is positive. The Altai krai, Omsk oblast, Novosibirsk oblast, and Krasnoyarsk krai have the greatest potential for sequestration. Several subjects of the Russian Federation—Krasnodar krai, Republic of Crimea, Rostov oblast, Primorskii krai, Republic of Adygea, and Kaliningrad oblast—demand the adoption of sustainable management of soil resources.

Multi-Scenario Land Use Optimization Simulation and Ecosystem Service Value Estimation Based on Fine-Scale Land Survey Data

Land optimization simulation and ecosystem service value (ESV) estimation can better serve land managers in decision-making. However, land survey data are seldom used in existing studies, and land optimization constraints fail to fully consider land planning control, and the optimization at the provincial scale is not fine enough, which leads to a disconnection between academic research and land management. We coupled ESV, gray multi-objective optimization (GMOP), and patch-generating land use simulation (PLUS) models based on authoritative data on land management to project land use and ESV change under natural development (ND), rapid economic development (RED), ecological land protection (ELP), and sustainable development (SD) scenarios in 2030. The results show that construction land expanded dramatically (by 97.96% from 2000 to 2020), which encroached on grassland and cropland. This trend will continue in the BAU scenario. Construction land, woodland, and cropland are the main types of land used for expansion, while grassland and unused land, which lack strict use control, are the main land outflow categories. From 2000 to 2030, the total amount of ESV increases steadily and slightly. The spatial distribution of ESV is significantly aggregated and the agglomeration is increasing. The policy direction and land planning are important reasons for land use changes. The land use scenarios we set up can play an important role in preventing the uncontrolled expansion of construction land, mitigating the phenomenon of ecological construction, i.e., “governance while destruction”, and promoting food security. This study provides a new approach for provincial large-scale land optimization and ESV estimation based on land survey data and provides technical support for achieving sustainable land development.

Macroeconomic impacts and co-benefits of deep-decarbonization in Thailand

The updated Nationally Determined Contributions (NDC) in 2022 of Thailand includes an aggressive GHG emission reduction target of 40% in 2030 from its baseline emissions. However, the macroeconomic impacts and co-benefits associated with reducing GHG emissions are not addressed. This study analyzes the macroeconomic implications and co-benefits of GHG emission reduction in Thailand to achieve the NDC and net zero emission (NZE) targets by 2050 using the AIM/Hub-Thailand model. This paper provides co-benefits for Thailand on ambitious long-term GHG emission reduction targets. Considering the co-benefit analysis in the policy documents will provide holistic insights on the positive impacts of GHG mitigation. Results show that Thailand would have to bear a GDP loss of 7.7% in 2050 compared to the BAU level if the net zero emissions need to be achieved. Fuel switching from fossil fuel to electricity in the demand side and improvement of technologies in the power sector also reduces air pollutant emissions. The increasing dependence on domestic energy supply in the NZE scenario will make the country less vulnerable to the fluctuating prices in the international energy market. In terms of trade-offs, the land use for sustainable biomass in both the NDC and NZE scenarios will be larger than in the BAU scenario. Results show better land use for biomass production and higher yields in agricultural production. Moreover, the achievement of NZE pathway will require effective usage of land area and better use of energy resources, thereby making the country more energy secure.

A study on energy-water-food-carbon nexus in typical Chinese northern rural households

The energy-water-food-carbon nexus of rural households requires different strategies from those of urban households. This study proposes a nexus framework and a quantitative method to analyze the energy, water, food, and carbon of typical northern rural households in China. A system dynamic model of the rural household energy-water-food-carbon nexus was developed. Three scenarios were designed, namely appliance, behavior, and price, with 42 sub scenarios in total to explore the medium to long-term dynamic changes in energy-water-food-carbon in rural households under different scenarios. The results of the appliance scenario show that there is a strong water-gas and food-gas nexus effect in northern rural households. Compared with the BAU scenario, reducing the consumption of meat and dairy by 5% can save 11.89% of gas and reduce 5.64% of carbon. Moreover, the policy simulation results indicate that a policy will affect not only its target subsystem, but also other subsystems, creating a policy nexus. The behavior adjustment scenarios have a strong nexus effect on water and energy, and resource price adjustment scenarios have a good effect on water and food consumption. Therefore, when making policies at the household level, policy makers need to look at each subsystem comprehensively, use the synergistic effect of policies skillfully and avoid the antagonistic effect of policies, in order to optimize the positive effect of policies.

Length-based stock assessment for Malabar blood snapper in Makassar Strait-Indonesia: Status and recommendation for sustainability

Snapper is one of the important groups of fish in demersal-reef fisheries in Indonesia. Malabar blood snapper Lutjanus malabaricus is the main and dominant species in snapper fisheries, making it the major target of fishing activities in Indonesia, including in Makassar Strait. Current management is still limited based on aggregate stocks (i.e., TAC for demersal-reef fish, snapper is included in this group), to reduce uncertainty, focused management is needed on specific fish stocks. The length-based approach for stock assessment was conducted under a data-limited situation. This study revealed the character of L.malabaricus in the Makassar Strait based on life history parameters as the categories of slow growth, late maturity and long-lived species. The condition of L.malabaricus has indicated a decrease in stocks based on several length models (LBSPR, LBB and LBI). A simulation of determining reference points for setting length size as a management intervention has been carried out with the scenarios: BaU (Lc=33.5 cm), Limit (Lc=Lm50=47.2 cm), and Target (Lc=Lc-opt =57 cm), but F is constant (F=0.5, as the current F). The BaU scenario performed it is difficult for L.malabaricus to achieve sustainability. In the limit scenario, MSY could be achieved by maintaining F=0.5 and SPR at 30% with Lc=Lm50. In the target scenario, there is no sustainability risk with FFSPR40%=0.5 and Lc=Lc-opt. It can be said that the limit reference point is determined at SPR 30% with Lc=Lm50, while the target reference point is at SPR 40% with Lc=Lc-opt.

Deep Learning-Based Carbon Emission Forecasting and Peak Carbon Pathways in China’s Logistics Industry

As a major energy-consuming industry, energy conservation and emission reduction in the logistics industry are critical to China’s timely achievement of its dual-carbon goals of “carbon peaking” by 2030 and “carbon neutrality” by 2060. Based on deep learning, Random Forest (RF) was used to screen out the key factors affecting carbon emissions in the logistics industry, and the Whale Algorithm-optimized Radial Basis Function Neural Network (WOA-RBF) was proposed. The Monte Carlo simulation predicted the future evolution trends of each key factor under the three scenarios of baseline scenario (BAU), policy regulation scenario (PR), and technological breakthrough scenario (TB) and accurately predicted the carbon emission trends of the logistics industry from 2023 to 2035 by using the most probable future values of each influencing factor as inputs to the WOA-RBF prediction model. The results of the study demonstrate that fixed asset investment (LFI), population (P), total energy consumption (E), energy consumption per unit of value added of the logistics industry (EIL), share of oil consumption (OR), and share of railway freight turnover (RTR) are the key factors influencing the logistics industry’s carbon emissions. Monte Carlo simulations can effectively reflect the uncertainty of future changes in these key factors. In comparison to the BAU and PR scenarios, the TB scenario, with the combined incentives of national policy regulation and technology innovation, is the most likely for the logistics industry to meet the “Peak Carbon” goal baseline scenario.

Research of the Impact of Hydrogen Metallurgy Technology on the Reduction of the Chinese Steel Industry’s Carbon Dioxide Emissions

The steel industry, which relies heavily on primary energy, is one of the industries with the highest CO2 emissions in China. It is urgent for the industry to identify ways to embark on the path to “green steel”. Hydrogen metallurgy technology uses hydrogen as a reducing agent, and its use is an important way to reduce CO2 emissions from long-term steelmaking and ensure the green and sustainable development of the steel industry. Previous research has demonstrated the feasibility and emission reduction effects of hydrogen metallurgy technology; however, further research is needed to dynamically analyze the overall impact of the large-scale development of hydrogen metallurgy technology on future CO2 emissions from the steel industry. This article selects the integrated MARKAL-EFOM system (TIMES) model as its analysis model, constructs a China steel industry hydrogen metallurgy model (TIMES-CSHM), and analyzes the resulting impact of hydrogen metallurgy technology on CO2 emissions. The results indicate that in the business-as-usual scenario (BAU scenario), applying hydrogen metallurgy technology in the period from 2020 to 2050 is expected to reduce emissions by 203 million tons, and make an average 39.85% contribution to reducing the steel industry’s CO2 emissions. In the carbon emission reduction scenario, applying hydrogen metallurgy technology in the period from 2020 to 2050 is expected to reduce emissions by 353 million tons, contributing an average of 41.32% to steel industry CO2 reduction. This study provides an assessment of how hydrogen metallurgy can reduce CO2 emissions in the steel industry, and also provides a reference for the development of hydrogen metallurgy technology.

The impact of carbon tax policy on residents’ welfare of China and its heterogeneity under the carbon neutrality goal: A CGE model-based analysis

Under the background that China will achieve the goal of carbon neutrality by 2060, analyzing the impact of carbon neutral target constraints on the welfare of urban and rural residents is an important part of the design of a carbon emission reduction policy mechanism, in this paper, the dynamic recursive CGE(Computable general equilibrium) model is used to simulate the changes in welfare level, income consumption, and CO2 emissions of residents and their urban-rural heterogeneity under the policy of imposing a carbon tax on the fossil energy industry from 2022 to 2060. The results show that: compared with the BAU scenario, by 2060, first the income and consumption of residents will decrease slightly. Second, the target constraint of carbon neutrality makes the equivalent variation and compensate variation of urban and rural residents' welfare decrease slightly, and the equivalent variation and compensate variation of urban residents decrease more than those of rural residents. The following conclusions are drawn: first, the carbon-neutral target constraint makes the welfare of urban and rural residents decline slightly, but it will not have a strong impact on residents’ income and consumption. Second, this constraint has a greater impact on the income, consumption and savings of urban residents than those of rural residents.

Assessing the distributional impacts of ambitious carbon pricing in China's agricultural sector

Non-CO2 greenhouse gas emissions from the agricultural sector play an important role in the global warming process. The carbon neutrality target pledged by China accelerates the research and implementation of climate mitigation policies. This study assessed the distributional impacts of carbon pricing on the agricultural sector from a multi-regional CGE model subdividing 12 primary agricultural sectors, 15 processed food sectors and 10 groups of households. The results revealed that in an agricultural carbon tax scenario under 1.5 °C target, emission-intensive food products (red meat and dairy products) experienced rapid price increases (4.22% and 3.14%) and consumption decreases (−6.61% and − 4.30%). The poorest rural households experienced the highest losses of 2.05% in protein and 1.42% in energy due to the decline in food consumption. Regressive welfare impacts were also observed, with the loss of rural residents being 1.3 times that of urban residents. The poorest income quintile tended to suffer a larger loss due to their higher share of food expenditures and greater vulnerability to changes in food prices. Furthermore, income inequality increased under the agricultural carbon tax, and the Gini coefficient increased by 0.57% compared with the BAU scenario in 2050, especially for the northern and southwestern regions. Tax revenue recycling focusing on the poor can help to offset the negative effects by expanding their income. Overall, a carefully designed carbon policy for the agricultural sector could help reduce carbon emissions while protecting vulnerably poor people and regions.

The value of ecosystem services in arid and semi-arid regions: A multi-scenario analysis of land use simulation in the Kashgar region of Xinjiang

This study aimed to investigate the interrelationships between land use/land cover (LULC) and ecosystem services value (ESV) in arid and semi-arid regions, with a specific focus on the Kashgar region in Xinjiang, China. Four distinct development patterns (BAU, RED, ELP, and EBB) were derived using a multi-objective planning algorithm (MOP), and the relationship between LULC and ESV was analyzed using three methods: the random forest algorithm, the PLUS model, and the ecosystem service equivalence method. The findings indicated a significant projected expansion of LULC in Kashgar by 2030, primarily concentrated in arable and construction land. The ELP scenario demonstrated the lowest growth in cultivated and urbanized land, while the BAU and RED scenarios exhibited higher increments. Notably, the expansion of arable and construction land was concentrated around the urban agglomeration of the Kashgar region, whereas changes in forest land were concentrated near the Kunlun Mountains. Regarding ESV, grassland and watersheds played a significant role, accounting for 45 % and 30 % of the total, grasslands and watersheds emerge as pivotal components in supporting biodiversity, soil preservation, water regulation, and purification. The study underscores the necessity of conserving these ecosystems. This study highlights the trade-offs between economic development and ecological/environmental protection. The RED scenario, driven by rapid economic growth and urbanization, has resulted in a significant decline in ESVs, underscoring the urgency of sustainable land use practices. The MOP-PLUS-ESV model used in this study provides a powerful framework for decision makers to make informed choices in land use planning. By prioritizing a balance between economic development and ecological preservation, the model contributes to sustainable community and regional growth, emphasizing the preservation of important ecosystems. The findings suggest that Kashgar needs to adopt a sustainable land use strategy that balances economic development and ecological/environmental protection to maximize the provision and protection of ESV. The pathway emphasizes the integrated consideration of ESV, rational land use policies, and enhanced ecological protection to achieve an ecological civilization.

Implementation of electric vehicles to support energy conservation and efficiency improvement in the transportation sector in Indonesia

More than 45.7% of Indonesia’s total energy consumption in 2021 came from the transportation sector. The transportation sector needs to improve energy efficiency and apply energy conservation measures in order to reduce energy consumption. The use of electric cars as an environmentally friendly replacement for internal combustion engine (ICE) vehicles is one effort in this direction. Using electric vehicles will reduce greenhouse gas emissions in the transportation sector; however, if fossil fuels continue to dominate the power sector, this will also lead to an increase in global GHG emissions. The power sector must employ NRE as much as possible in order to cut emissions. The energy system model, which is projected to be operational until the year 2100, was created using the LEAP and NEMO tools. Power plants are optimized for electricity delivery using the BAU and NZE scenarios. As a result of the energy system’s optimization, the usage of petroleum products will be reduced by about 778.7 million BOE in 2100, and the transportation sector’s GHG emissions will be reduced by 304.4 million tons of CO2e. By promoting energy efficiency, conservation, and the environment’s impact, the use of electric vehicles can improve Indonesia’s NZE situation in the transportation sector.

Ecosystems face the risk of ecological deficits in the southern foothills of the Himalayas

As global demand for ecological resources continues to surpass ecosystem capacity, predicting future changes in ecological supply, consumption, and carrying capacity is crucial for informed decision-making regarding sustainable land use and ecological security. This study forecasts ecological carrying states in Nepal, a representative region in the southern Himalayan foothills, from 2020 to 2030. We employ three alternative scenarios from the Shared Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP): SSP2-RCP4.5 (BAU), SSP1-RCP2.6 (TSS) and SSP5-RCP8.5 (SSS). Our findings indicate a continuous rise in ecological consumption, particularly in densely populated tropical areas such as the hilly region (HR) and Terai region (TR). Under the BAU and SSS scenarios, ecological supply decreases rapidly in these regions. Alarmingly, over one-third of Nepal's districts, mainly in central and eastern HR and TR, are projected to experience overloaded ecological carrying states by 2030. Climate change sensitivity and escalating consumption due to improved living standards and population growth pose significant challenges for these regions to reverse ecological deficits. This study offers a scientific foundation for reconciling the conflict between ecological protection and societal demands, facilitating the preservation, restoration, and sustainable use of terrestrial ecosystems while striving to achieve the Sustainable Development Goals by 2030.

Multi-objective optimization of capacity and technology selection for provincial energy storage in China: The effects of peak-shifting and valley-filling

To support long-term energy storage capacity planning, this study proposes a non-linear multi-objective planning model for provincial energy storage capacity (ESC) and technology selection in China. The model aims to minimize the load peak-to-valley difference after peak-shaving and valley-filling. We consider six existing mainstream energy storage technologies: pumped hydro storage (PHS), compressed air energy storage (CAES), super-capacitors (SC), lithium-ion batteries, lead-acid batteries, and vanadium redox flow batteries (VRB). The optimized results show that 1) the accumulated ESC in China could increase at an average annual rate of 14.4–17.6% in 2020–2035, reaching 271.1–409.7 GW in 2035 at a total cost of 34.9–41.6 trillion yuan. 2) With favorable technical development levels and application prospects, lithium-ion batteries will be the dominant technology in 2035 in all scenarios. SC has the fastest growth rate among the six technologies in most scenarios because of its flexible location and long lifetime. 3) In the BAU scenario, Xinjiang, Inner Mongolia, Hebei, Ningxia, and Qinghai are the provinces with the largest ESC in 2035 while the four municipalities of Chongqing, Beijing, Tianjin, and Shanghai, as well as Tibet, have the smallest. This layout depends on the differences in the installed capacity of renewable energy in the provinces.

A life cycle assessment of biological treatment scenario of municipal solid waste in developing country (case study: Makassar, Indonesia)

Municipal Solid Waste Management (MSWM) is a significant challenge in developing countries, including Indonesia, where landfilling is the predominant waste treatment method. This study examines the case of Makassar City, where landfilling is still in use while composting is utilized to a limited extent. The research aims to evaluate and compare the environmental impact with three alternative scenarios involving biological treatment and life cycle assessment (LCA) processing. The scenarios were examined: Business as Usual Scenario (BaU), Landfill and Composting, Landfill and Anaerobic Digestion, Landfill, Composting, and Anaerobic Digestion. The study considers waste transportation, landfilling, anaerobic digestion, and composting within its system boundary, using annual waste processing as the functional unit. Environmental impacts assessed include global warming, acidification, and eutrophication. The findings indicate that the BaU scenario has the highest environmental impact, particularly regarding global warming, with 8,436,685.61 kg CO2eq/year emissions. On the other hand, alternative scenario 3, which incorporates landfill management, composting, and anaerobic digestion, shows a relatively lower Global Warming Potential (GWP) emission. However, further measures are needed to effectively reduce emissions, such as implementing a cover for the compost pile and arranging the mixing.

Energy Demand Modeling for the Transition of a Coal-Dependent City to a Low-Carbon City: The Case of Ulaanbaatar City

Cities have committed to reducing greenhouse gas emissions and promoting renewable energy. However, many cities continue to rely on fossil fuels, while renewable energy sources are not used or are unable to meet the demand that fossil fuels provide. Depending on the geographic location, climate, and resources, cities must find their own path to energy sustainability. The city of Ulaanbaatar is one of the coal-dependent cities, its electricity and heat consumption mainly coming from coal. In this study, the future final energy demand of a coal-dependent city is identified and analyzed to make it a low-carbon city. Long-term energy demand projections for Ulaanbaatar to 2050 are conducted using the model for analysis of energy demand (MAED) model. Four scenarios are developed based on the existing local and national policies in the socio-economic and energy sectors, as well as more ambitious policy and technology measures recommended by various studies in the MAED_D model. The final energy demand is calculated to be 548, 460, 334, and 264 PJ in 2050 for BAU, REF, NDC, and RM scenarios, respectively, compared to 135 PJ in 2020. The results show that the high penetration of electricity and renewable energy, energy efficiency measures, and energy intensity reduction in all sectors can significantly reduce the future energy demand and help the transition towards a low-carbon city.