Cross-scale trade-off analysis for sustainable development: linking future demand for animal source foods and ecosystem services provision to the SDGs

Dietary transition towards higher consumption of animal source foods (ASF) associated with higher incomes across low and middle-income countries could have negative impacts on environmental systems and their potential in the long run to provide services necessary for achieving multiple Sustainable Development Goals (SDGs). In this article, we integrate economic, land use allocation, and biophysical models to investigate trade-offs between the five ecosystem services and their contributions to various SDGs associated with agricultural expansion to meet future demand for ASF, using Tanzania as a case study. Our results show that under the scenario of sustainable socio-economic development, between 2010 and 2030 in Tanzania, per capita income grows by 169% and the share of population at risk of hunger declines from 34.8% to 23%. These changes can be associated on a macro-level with positive contributions to achievement of SDG 1 (No Poverty) and SDG 2 (Zero Hunger). To satisfy feed demand for increased livestock production domestically, an increase by 21.4% of biomass production as compared to 2010 is needed. Analysis of alternative scenarios for meeting this new demand shows potential threats on a landscape level to achieving numerous SDGs and more generally to attaining sustainable food systems. Ecosystem-based contributions primarily decline to SDGs: SDG 3 (Health), SDG 6 (Clean Water), SDG 11 (Sustainable Cities), SDG 13 (Climate) and SDG 15 (Terrestrial Life). We find that higher crop productivity and redesign of agro-ecosystems to increase on-farm tree cover could significantly limit these losses. Alternatively, the growing demand for ASF could be satisfied with imports, which would allow for reducing the trade-offs locally. However, this would result in at least partially only displacing ecosystem service losses to the exporting countries.

Implementasi Aplikasi Sistem Peramalan Persedian Barang Menggunakan Metode Single Moving Average Berbasis Web

Forecasting is an estimate of future demand based on several forecasting variables based on historical time series or a process of using historical data (past data) that has been owned to use this model and use this model to estimate future conditions.The Ivori mini market SME group is known to be a mini market that sells daily necessities. The goods provided by the ivori mini market are not focused on only one type of goods, but include all types of goods. Ivori mini market often runs out of stock because there is no inventory planning. The main purpose of making this application is to assist employees in determining inventory planning that must be provided next month. While the method used to make this forecast is a single moving average, one of the time series methods in forecasting. Single Moving Average is a forecasting method that is done by collecting a group of observed values, looking for the average value as a forecast for the future period. The result of this forecasting is to predict the number of sales that will occur in the coming month.

Can Substitutes Reduce Future Demand for Wildlife Products: A Case Study of China’s Millennial Generation

China is one of the world’s leading consumer markets for wildlife products, yet there is little understanding of how demand will change in the future. In this study, we investigate the consumptive habits and attitudes of the millennial ‘Juilinghou’ demographic – a subset of society in China with the potential to substantially influence future demand for wildlife products. We surveyed 350 Chinese university students across Harbin and Beijing, China, and found that the intended future consumption of wildlife products was relatively low in this population but with a strong orientation towards wildlife products with medicinal properties. Seventy percent of those respondents who had used and/or intended to use wildlife products were willing to try substitutes, but this was heavily dependent on their price (cheaper) and quality. The insights gained through this survey are intended to meaningfully inform future initiatives to introduce sustainable substitutability into wildlife markets to alert future wildlife product consumers to alternative choices.

Commercial Energy Demand Forecasting in Bangladesh

Although both aggregate and per capita energy consumption in Bangladesh is increasing rapidly, its per capita consumption is still one of the lowest in the world. Bangladesh gradually shifted from petroleum-based energy to domestically sourced natural-gas-based energy sources, which are predicted to run out within next two decades. The present study first identified the determinants of aggregate commercial energy and its three major components of oil, natural gas, and coal demand for Bangladesh using a simultaneous equations framework on an annual database covering a period of 47 years (1972–2018). Next, the study forecast future demand for aggregate commercial energy and its three major components for the period of 2019–2038 under the business-as-usual and ongoing COVID-19 pandemic scenarios with some assumptions. As part of a sensitivity analysis, based on past trends, we also hypothesized four alternative GDP and population growth scenarios and forecast corresponding changes in total energy demand forecast. The results revealed that while GDP and lagged energy demand are the major drivers of energy demand in the country, we did not see strong effects of own- and cross-price elasticities of energy sources, which we attributed to three reasons: subsidized low energy prices, time and cost required to switch between different energy-mix technologies, and suppressed energy demand. The aggregate energy demand is expected to increase by 400% by the end of the forecasting period in 2038 from its existing level in 2018 under the business-as-usual scenario, whereas the effect of COVID-19 could suppress it down to 300%. Under the business-as-usual scenario, the highest increase will occur for coal (3.94-fold), followed by gas (2.64-fold) and oil (2.37-fold). The COVID-19 pandemic will suppress the future demand of all energy sources at variable rates. The ex ante forecasting errors were small, varying within the range of 3.6–3.7% of forecast values. Sensitivity analysis of changes in GDP and population growth rates showed that forecast total energy demand will increase gradually from 3.58% in 2019 to 8.79% by 2038 from original forecast values. Policy recommendations include capacity building of commercial energy sources while ensuring the safety and sustainability of newly proposed coal and nuclear power installations, removing inefficiency of production and distribution of energy and its services, shifting towards renewable and green energy sources (e.g., solar power), and redesigning subsidy policies with market-based approaches.

Projecting Saudi Sectoral Electricity Demand in 2030 Using a Computable General Equilibrium Model

Projecting future demand for electricity is central to power sector planning, as these projections inform capacity investment requirements and related infrastructure expansions. Electricity is not currently economically storable in large volumes. Thus, the underlying drivers of electricity demand and potential market shifts must be carefully considered to minimize power system costs.

Smart Transit Dynamic Optimization and Informatics

With the popularity of Big Data and urban informatics, there is increasing interest in ways to use real time data to improve transportation system operations. In many real-wold applications, demand is revealed dynamically over time, and consequently the routes are determined dynamically as well. In this thesis, contributions are made to several key components of a “smart” transit system framework where dynamic operations are driven by real time information. The first component is in dynamic routing and pricing of a fleet of vehicles. A new dynamic dial-a-ride policy is introduced that features non-myopic pricing based on optimal tolling of queues to fit with the multi-server queueing approximation method. By including social optimal pricing, the social welfare of the resulting system outperforms a pricing policy based on the marginal cost increase of a passenger over a range of test instances. In the examples tested, improvements in social welfare of the non-myopic pricing over the myopic pricing were in the 20% - 31% range. The second component is in the informatics. Effective dynamic optimization of a system (routing, scheduling, fare setting, etc.) requires effective short term prediction of traveler/customer arrival using real-time data. Several recent methods for arrival process prediction, both offline and online, are investigated using real taxi data from New York. An experiment is conducted using the same data set to draw comparisons for arrival process modeling, suggesting that the temporal seasonal factors method from Ihlers et al. (2006) is more effective as an offline approach and the IntGARCH method from Matteson et al. (2011) is more effective as an online approach. The third component investigated is in the prepositioning of idle vehicles. Vehicles that are positioned at locations that take into account future demand can lead to reduced wait times for passengers and improved level of service. A dynamic relocation model is proposed that includes queueing delay to approximate the congestion effect of future demand. A linear problem is formulated based on Marianov and Serra’s (2002) work. By varying customer arrivals, the approach provides a new managerial tool to find the optimal service level.

Smart Transit Dynamic Optimization and Informatics

With the popularity of Big Data and urban informatics, there is increasing interest in ways to use real time data to improve transportation system operations. In many real-wold applications, demand is revealed dynamically over time, and consequently the routes are determined dynamically as well. In this thesis, contributions are made to several key components of a “smart” transit system framework where dynamic operations are driven by real time information. The first component is in dynamic routing and pricing of a fleet of vehicles. A new dynamic dial-a-ride policy is introduced that features non-myopic pricing based on optimal tolling of queues to fit with the multi-server queueing approximation method. By including social optimal pricing, the social welfare of the resulting system outperforms a pricing policy based on the marginal cost increase of a passenger over a range of test instances. In the examples tested, improvements in social welfare of the non-myopic pricing over the myopic pricing were in the 20% - 31% range. The second component is in the informatics. Effective dynamic optimization of a system (routing, scheduling, fare setting, etc.) requires effective short term prediction of traveler/customer arrival using real-time data. Several recent methods for arrival process prediction, both offline and online, are investigated using real taxi data from New York. An experiment is conducted using the same data set to draw comparisons for arrival process modeling, suggesting that the temporal seasonal factors method from Ihlers et al. (2006) is more effective as an offline approach and the IntGARCH method from Matteson et al. (2011) is more effective as an online approach. The third component investigated is in the prepositioning of idle vehicles. Vehicles that are positioned at locations that take into account future demand can lead to reduced wait times for passengers and improved level of service. A dynamic relocation model is proposed that includes queueing delay to approximate the congestion effect of future demand. A linear problem is formulated based on Marianov and Serra’s (2002) work. By varying customer arrivals, the approach provides a new managerial tool to find the optimal service level.