Total Social Cost Research Articles

Climate Change and Human Response to Sustainable Environmental Governance Policy: Tax or Emissions Trading?

With climate change, humans are looking for effective ways to improve the ecological environment and provide comfortable survival space for sustainable development. There are two main economic methods for controlling environmental pollution: emissions fees (Pigouvian taxes) and emissions standards (emissions trading). However, the two policies have their applicable conditions in dealing with different sources of pollution and dynamic ecosystems, and many problems will arise if they are misused. This paper theoretically proves that significant (minor) pollution sources could satisfy the condition that the benefit function’s curvature is greater (less) than that of the cost function. When the speed of ecological absorption is constant, a price policy controlling significant pollution sources will generate uncertainty; the quantity policy will generate a higher total social cost of managing the minor pollution source. When the speed of ecological absorption is not constant, adjusting the number of part pollution permits will lead to two kinds of pollutant leakage. If the pollution permits can be freely circulated, it will lead to the pollution of regional b (less regulated areas) inflow into the region a (more regulated areas); if permits are not freely circulated, otherwise not.

An optimal control policy in fighting COVID-19 and infectious diseases

When an outbreak starts spreading, policymakers have to make decisions that affect the health of their citizens and the economy. Some might induce harsh measures, such as a lockdown. Following a long, harsh lockdown, the recession forces policymakers to rethink reopening. To provide an effective strategy, here we propose a control strategy model. Our model assesses the trade-off between social performance and limited medical resources by determining individuals’ propensities. The proposed strategy also helps decision-makers to find optimal lockdown and exit strategies for each region. Moreover, the financial loss is minimized. We use the public sentiment information during the pandemic to determine the percentage of individuals with high-risk behavior and the percentage of individuals with low-risk behavior. Hence, we propose an online platform using fear-sentiment information to estimate the personal protective equipment (PPE) burn rate overtime for the entire population. In addition, a study of a COVID-19 dataset for Los Angeles County is performed to validate our model and its results. The total social cost reduces by 18% compared with a control strategy where susceptible individuals are assumed to be homogeneous. We also reduce the total social costs by 26% and 22% compared to other strategies that consider the health-care cost or the social performance cost, respectively.

Seismic Resilience Assessment Strategy for Social and Sustainability Impact Evaluation on Transportation Road Network: A Seismic Liquefaction-Induced Damage Application

Transport networks play a critical role for living communities, as they facilitate the exchange of people and goods and foster economic growth. Improving their resilience against seismic hazards, among which liquefaction is by far one of the most significant and complex, is consistent with most of the Sustainable Development Goals pinpointed by the United Nations’ Agenda. In this paper, an original methodological framework, combining innovative Geo-statistical approaches to analyze soil properties, prediction models for soil liquefaction, and calibrated transport demand models providing the social and economic cost associated with seismic-induced road damages and closures within a renewed Geographical Information Systems (GIS) workspace, is proposed. In particular, based on traditional risk assessment evaluation, an innovative approach to evaluate the exposure in terms of economic loss due to lack of accessibility is presented. The methodology is applied to a district area in northern Italy that underwent a recent seismic event that caused several soil liquefaction phenomena. Results provided by a sensitivity analysis on a stochastic (return period) basis are derived: as the seismic intensity increases, the total social costs increase, but the trend of the rates due to traffic delays and the loss of accessibility are irregular. Although further simulation scenarios need to be undertaken, the proposed methodology seems to provide an effective planning tool to evaluate preventive strategies aimed at improving the resilience of transport networks against liquefaction risk.

Shadow-Price DRL: A Framework for Online Scheduling of Shared Autonomous EVs Fleets

This paper studies the online scheduling of shared autonomous electric vehicle (SAEV) fleets. The study includes charging management, routing and rebalancing strategies for SAEVs to serve the trip demands in the coupled power and transportation network (PTN). It aims to minimize the total social cost of PTN. The difficulty lies in how to deal with time-varying trip demands with the time-coupled SAEV scheduling and PTN operation considered. To address this challenge, for the first time, we propose a novel framework named the shadow-price deep reinforcement learning (shadow-price DRL), which combines the rigorous PTN operation model and the data-driven model-free DRL-based algorithm. Within the shadow-price DRL, the policy neural network adaptively learns the system dynamics and imposes its actions on the online SAEV scheduling problem as the dynamic shadow prices. By doing so, the SAEV schedule will be determined in the online manner. In addition, the connection between the proposed framework and Lagrangian Relaxation method is discussed, which illustrates the principles and effectiveness of the proposed method. The case studies include the practical Xi’an city which verifies the effectiveness of the shadow-price DRL and illustrates its significant superiority over the safe DRL and model predictive control (MPC) based methods.

Joint expansion planning of distribution network with uncertainty of demand load and renewable energy

Rational planning plays a key role in developing and improving operation security and stability of distribution networks (DNs). This paper proposes a novel method to solve the expansion of transmission lines (TLs) and the allocation of distributed generators (DGs) considering the uncertainty of load demand and renewable energy. The spanning-tree is a theoretical method specifically tailored to guarantee the radiality operation of distribution networks. Also, to guarantee the stability and reliability of the network, the probability function is used to describe the stochastic nature of load behavior and renewable sources fluctuation. Thus, the most proper installation location and time for TL and DG assets can be identified by modeling the joint expansion planning. The objective of the proposed model is minimizing the present value of the total social costs, including the cost of investment, purchasing energy, and environmental benefits. This mixed-integer nonlinear problem (MINLP) optimization model is solved based on GAMS environment. The effectiveness and scalability of this model is validated and illustrated based on the IEEE RTS-24 system.

Heterogeneity in preferences for outcomes of integrated care for persons with multiple chronic diseases: a latent class analysis of a discrete choice experiment

PurposeFor an integrated care programme to be successful, preferences of the stakeholders involved should be aligned. The aim of this study is to investigate to which extent outcomes beyond health are valued and to study the heterogeneity of preferences of those involved in integrated care.MethodsA discrete choice experiment (DCE) was conducted to elicit preferences for eight Triple Aim outcomes, i.e., physical functioning, psychological well-being, social relationships & participation, enjoyment of life, resilience, person-centeredness, continuity of care and total health and social care costs. Stakeholders were recruited among Dutch persons with multi-morbidity, informal caregivers, professionals, payers, and policymakers. A Bayesian mixed-logit model was used to analyse the data. Subsequently, a latent class analysis was performed to identify stakeholders with similar preferences.Results739 stakeholders completed the DCE. Enjoyment of life was perceived as the most important outcome (relative importance: 0.221) across stakeholders, while total health and social care costs were perceived as least important (0.063). The latent class analysis identified four classes. The first class (19.9%) put most weight on experience with care outcomes. The second class (39%) favoured enjoyment of life. The third class (18%) focused relatively more on physical health. The fourth class (24%) had the least consistent preferences.ConclusionThis study has highlighted the heterogeneity in views of stakeholders in integrated care on what is important in health(care) for persons with multi-morbidity. To accurately value integrated care a variety of outcomes beyond health–e.g., enjoyment of life and experience with care–should be taken into account.

Layout Optimization for Shared Parking Spaces Considering Shared Parking Walking Time and Parking Fee

Shared parking is an effective means to alleviate the shortage of parking spaces in urban central areas during the morning peak hours. Meanwhile, walking time cost and parking fees are two critical factors affecting shared parking efficiency. Firstly, based on the classic bottleneck model, walking time cost and shared parking fees are added to the model, then the user equilibrium solutions are obtained considering two choices of parking lots: accessorial parking and shared parking. Next, taking the minimum total social cost and the minimum total queue time as the management goals, respectively, the quantitative relationship between parking fee as well as the dispersion degree of the shared parking spaces with the optimal travel pattern is proved. Besides, the rules and methods for the reasonable setting of shared parking fee and the dispersion degree of shared parking spaces layout are proposed. Through the research, it was demonstrated that: (1) differentiated shared parking fee based on the capacity of the accessorial parking lot can reduce both the total social cost and road congestion, while total social cost increases with the dispersion of the layout of shared parking spaces and road congestion decreases with the dispersion of the layout of shared parking spaces; and (2) when derived the optimal travel pattern, it is found that there is an inert zone of shared parking fee, i.e., regardless of adjusting shared parking fee, it had no impact on the determination of the optimal travel pattern. Finally, we put forward policy recommendations combining the numerical examples. Considering the total social cost and congestion, managers can improve the utilization efficiency of shared parking spaces while taking into account social benefits by reasonably setting shared parking fee and the dispersion degree of shared parking spaces layout.

Transactive Energy Sharing in a Microgrid via an Enhanced Distributed Adaptive Robust Optimization Approach

The rapid growth of microgrids with various distributed energy resources (DERs) brings new opportunities for local energy sharing in the microgrids. However, the uncertainties of renewable distributed generation and loads pose a great technical challenge for a microgrid operator (MGO). Thus, this paper proposes a transactive energy sharing (TES) approach for the MGO and DER aggregators to minimize the total social cost, considering network operating constraints. Accordingly, a two-settlement transactive energy (TE) market with an incentive energy pricing scheme is developed to encourage the participation in the energy sharing. Besides, the real-time energy transactions of the aggregators are considered in a day-ahead optimization stage to address their negative impacts on microgrid operation. To solve the proposed TES problem, an alternating direction method of multipliers (ADMM) is applied. The uncertainties in each ADMM local problem are further addressed by an adaptive robust optimization (ARO) method solved by a column-and-constraint generation (C&CG) algorithm. The updated dual and coupling variables at each ADMM iteration could interact with the C&CG algorithm, impairing ADMM convergence. To solve this issue, an alternating uncertainty-update procedure is developed. The simulation results verify the high efficiency and solution robustness of the proposed TES method.

A Bus Subsidy Scheme Design Model Considering Competition between Bus Companies

The competition between bus companies plays an important role in the effects of bus subsidy schemes for operation-sharing networks. This paper presents a bi-level programming model considering the competition to design bus subsidy schemes. In the upper sub-model, a subsidy scheme is given with the target of minimizing the total social costs (TSC). Further, in the lower sub-model, each bus company competes with other companies under the subsidy scheme. The competition is portrayed as a non-cooperative game where each company changes the departure frequency of their lines separately to obtain the maximum profit. The subsidy schemes can minimize the TSC when all bus companies pursue the maximum profit. A specific approach combining genetic algorithm and best response dynamics is proposed to solve the model. To validate the constructed model, a real-world case study is conducted in Guangzhou. From the results, as the scheme is implemented, the bus companies will be inspired to increase the line departure frequency, leading to a reduction of the passenger waiting time and performing a higher level of service. The TSC are reduced by 28.6%, among which the total waiting time costs are reduced by 43.0%. The model can guide bus subsidy scheme design for operation-sharing bus networks.

Multi-aspect efficiency measurement of multi-objective energy planning model dealing with uncertainties

In this study on energy planning, a combination of multi-objective optimization and efficiency measurement is proposed as a means for determining an efficient energy mix that considers the multi-dimensional nature of energy planning and its associated uncertainties. Various multi-objective functions are appended to the proposed optimization model to meet requirements related to energy need, cost, environmental impact, security, social impact, and social benefit. A slacks-based measure methodology is applied to determine the best energy mix from the alternatives produced by the appended model. The energy efficiency of each energy mix is measured from the linear combination of its defined inputs and outputs. The outputs to be maximized include total generated electricity, direct employment, and percentage of generated electricity from renewable energy, while the inputs to be minimized consist of total economic cost, carbon dioxide emission, total social cost, and power-plant-type dependence score. To demonstrate the applicability of the proposed model, a case study of Thailand’s power development plan is featured. Various types of power plants, both fossil fuel-fired and renewable energy-driven are considered in the empirical analysis. The results show that the proposed method can contribute significant improvements, including a reduction in total emissions and in the power-plant-type dependence score (by 31.41% and 25.59%, respectively). It also increases total employment and the proportion of generated electricity from renewable energy plants (by 25.73% and 47.39%, respectively), with marginal tradeoffs of total costs and total social costs (which increase by 8.94% and 13.89%, respectively). Quantitative results from the model could help policy makers efficiently determine an appropriate energy policy—one that optimizes all the various aspects, under a given set of constraints and scenarios of uncertainty.

Peer-to-Peer Energy sharing Among Smart Energy Hubs in an integrated Heat-Electricity Network

By increasing the penetration level of the combined heat and power systems and demand response programs between energy consumers in the distribution network, the interaction between electricity and the natural gas network becomes more complicated. This paper proposes a peer-to-peer (P2P) energy sharing scheme for energy trading among Smart Energy Hubs (S.E. Hubs) which can trade both electrical and thermal energy with each other to reduce their cost and, reduce their dependency to gas and electricity utility companies. A two-stage energy strategy is presented. In the first stage, the S.E. Hubs total social cost is reduced by finding the optimal energy sharing profiles. And, in the second stage, we use energy sharing profiles to obtain the optimal payments among S.E. Hubs in a fair manner, which encourages them to participate in energy sharing. The numerical study illustrates that the proposed method significantly reduces the smart energy hubs total social cost by 14%.

Location optimization of electric vehicle charging stations: Based on cost model and genetic algorithm

Nowadays, the development of new energy science has gradually entered a good situation, and accordingly, electric vehicles have been recognized by people all over the world and rapidly popularized in various countries. However, the rapid growth of electric vehicles has caused a series of problems, such as insufficient number of charging stations, uneven distribution, and high cost, which are becoming increasingly serious. In order to solve the above problems, this paper takes Ireland as an example for research. Based on the collection of relevant information, this paper first tries to build a social total cost model, and calculates the total operating cost of charging stations under various distribution conditions. In this model, the total social cost is subdivided into comprehensive economic cost and environmental cost. Economic costs include construction costs and fees, while environmental costs include electricity consumption and carbon dioxide emissions. Secondly, this paper established a charging station location optimization model based on genetic algorithm, which simplified the Irish territory into a rectangle with a length of 350 km and a width of 200 km, subdivided it into small squares, and adjusted the correlation coefficient to meet the charging demand. This solution solves the minimization problem of operating cost under the constraints of depreciation period of charging station, power consumption per unit distance of charging station and vehicle charging probability. Finally, in order to determine the factors that may have a significant impact on the total cost of charging stations, we conducted sensitivity analysis and found that the total cost is very sensitive to the number of charging stations and the possibility of EV charging per day.

Assessment on the Cost Synergies and Impacts among Measures on Energy Conservation, Decarbonization, and Air Pollutant Reductions Using an MCEE Model: A Case of Guangzhou, China

Many challenges are faced in the process of urban sustainable development, including the continuous growth in energy demand and rapid increase in CO2 and air pollutant emissions. This study focuses on the costs of measures to address these issues and establishes a multi-objective comprehensive assessment model for energy saving, CO2, and pollutant emission (MCEE). Taking Guangzhou as an example, the sustainable development measures are divided into three categories, energy-saving, demand-optimization, and environmental-protection. Five scenarios are set to quantitatively evaluate the costs when these measures are implemented alone or coordinately for the period 2015–2035. Conclusions are as follows: (1) Measures of energy-saving and demand-optimization have the best synergistic effect on energy saving and emission reduction. The synergistic benefits include an 80% and 84% increase in energy savings and CO2 reductions, respectively, and more than 50% increase in pollutant reductions. (2) Measures of demand-optimization and energy-saving have the best synergistic effect on cost saving, which reduces the unit technical improvement costs of energy saving and CO2 reduction by 49.5% and 54.9%, respectively, and the unit end-of-pipe costs of four pollutants by 59.15%, 54.43%, 61.15%, and 51.96, respectively. (3) Environmental-protection measures have remarkable synergistic effects in reducing the cost of health loss and labor loss. At the price of a 5% increase in technical improvement cost and 9% in end-of-pipe treatment cost, health loss, labor loss, and total social cost will be reduced by 18%, 19%, and 3%, respectively. The above conclusions provide support for cities of the same type to coordinate various measures, reduce resistance and barriers to their implementation, compensate for the market deficiency of high costs of some measures, and achieve the goal of sustainable development.

Minimal utilization rate for railway maintenance windows: a cost-benefit approach

Methods for economic assessment are often used in the rail sector to evaluate large infrastructure investments such as new high-speed lines. With larger networks and ageing infrastructure, these methods can also be used for planning maintenance. In this paper, we focus on the newly introduced concept of maintenance windows in Sweden. These are pre-allocated slots in the train timetable dedicated to performing, among others, periodic/frequent maintenance activities. To justify the pre-allocation of such windows, this study presents a method to find minimal utilization rates depending on window designs and traffic situations. Using a cost-benefit approach, the windows are assessed using a total social cost including work costs, loss in traffic production and reliability gains in future traffic. Based on a case study from the Southern main line in Sweden, we study the minimal utilization rate in different test scenarios, i.e., night or day shifts, asset degradation functions and designs of maintenance windows. The results show that lower utilization rates (4-42%) can be accepted during low-volume traffic or for partial closures, while higher utilization rates (47-83%) are required for full closures during high-volume traffic. Whether the rates are measured as the share of used window time or the share of utilized windows is less important, especially when higher utilization is required. Sensitivity analyses of asset knowledge indicate that parameters such as asset degradation function and minimum asset quality (and to a lesser extent traffic volume, discount rate and failure likelihood) can have a substantial effect on the minimum required utilization rates.

The monetary impact of zoonotic diseases on society: The Turkish Case

In this study, the burden of disease, costs, and animal losses caused by the seven most common zoonoses in humans and the two most common zoonoses in animals are calculated between 2016-2018 in Turkey. It aims to contribute to the literature by providing a holistic framework on the costs and burden of diseases of zoonoses in Turkey. The methodology of the study was based on the formula of ''Disease Burden of Zoonotic Diseases'' developed by the FAO. It was calculated under ''Burden of Early Mortality in Humans'', ''Burden of Morbidity in Humans'', "Financial Value of Lost Animals" and "Reduction in Production Capacity of Infected Animals". All cases which were registered in 2016, 2017 and, 2018 from the Ministry of Health (MoH) and the Ministry of Agriculture and Forestry (MoAF) concerning the relevant diseases were included in the study. It is found that the DALYs of all related zoonotic diseases increased and the costs for diagnosis, treatment, and prevention also rose between 2016-2018. The share of total social cost in the GDP of Turkey was estimated to be 0.0090% in 2016, 0.0097% in 2017, and 0.0113% in 2018. It is argued that the seven zoonoses in the scope of this study have an increasing burden graph on Turkish society between 2016-2018. Moving from the fact that most infectious diseases that threaten human and community health are of zoonotic origin and difficulties in predicting when, where or how a zoonotic disease will occur, all sectors should continue to carefully monitor events related to zoonoses and carry out joint studies.

Modeling traffic dynamics in periphery-downtown urban networks combining Vickrey's theory with Macroscopic Fundamental Diagram: user equilibrium, system optimum, and cordon pricing

This paper constructs a dynamic model for a periphery-downtown urban network combining Vickrey's theory with Macroscopic Fundamental Diagram (MFD) to capture interactions between the peripheral traffic and the downtown, and to examine how travelers on different corridors compete for the downtown road resources in the context of dynamic user equilibrium. In no-toll equilibrium if existing, when there exists no congestion delay in the downtown area, the departures for each corridor are regulated only by its bottleneck's capacity on the periphery of the downtown area, as occurred in Vickrey's bottleneck model, whereas when the downtown accumulation goes beyond the critical value, the departures for each corridor are constrained not only by its bottleneck's capacity but also by the downtown congestion level, regulated by all corridors’ departures in turn. We show that under specific assumptions, at the system optimum, the entrances at the boundary of the downtown area should be running at their full capacities before the onset of the downtown congestion, and the downtown network should be operating upon the onset of the downtown congestion at the critical accumulation with maximum production and highest traveling speed. Two optimal time-varying cordon pricing schemes (entrance-independent and entrance-dependent respectively) are developed to minimize the total social cost, which includes the queuing time costs concerning all corridors, the moving time cost in the interior of the downtown area, and schedule delay cost. What's more, the two time-varying cordon tolls to support the system optimum are analyzed from practical views. In the end, analytical results are illustrated and verified with numerical experiments.

Departure Time Choice Equilibrium and Tolling Strategies for a Bottleneck with Stochastic Capacity

This paper develops a bottleneck model in which the capacity of the bottleneck is assumed to be stochastic and follow a general distribution that has a positive upper bound. The user equilibrium principle in terms of mean trip cost is adopted to formulate commuters’ departure time choice in the stochastic bottleneck. We find that there exist five possible equilibrium departure patterns, which depend on both commuters’ unit costs of travel time, schedule delay early and late, and the uncertainty of the stochastic capacity of the bottleneck. All possible equilibrium departure patterns are analytically derived. Both the analytical and numerical results show that increasing the uncertainty of the capacity of the bottleneck leads to an increase of commuters’ individual mean trip cost. In addition, both a time-varying toll scheme and a single-step coarse toll scheme are designed within the proposed stochastic bottleneck model. We provide an analytical method to determine the detailed toll-charging schemes for both toll strategies. The numerical results show that the proposed toll schemes can indeed improve the efficiency of the stochastic bottleneck in terms of decreasing mean total social cost, and the time-varying toll scheme is more efficient than the single-step coarse toll scheme. However, as the uncertainty of the capacity of the bottleneck increases, the efficiency of the time-varying toll scheme decreases, whereas the efficiency of the single-step coarse toll scheme fluctuates slightly.

Multistage and Dynamic Layout Optimization for Electric Vehicle Charging Stations Based on the Behavior Analysis of Travelers

Electric vehicles (EV) are growing fast in recent years with the widespread concern about carbon neutrality. The development of charging infrastructures needs to be in phase with EV both in terms of quantity and charging time to decrease the range anxiety of EV users and resource waste. This paper proposed a multistage and dynamic layout optimization model based on mixed integer linear programming (MILP) for EV charging stations (CSs) to minimize the total social costs (TSC) consisting of the detour cost of EV users and the construction, relocation, and operating cost of CSs. The charging satisfaction coefficient and M/M/S/K model of queuing theory has been introduced to determine the desirable charging supply. The spatial-temporal distribution of charging demand was modeled based on the behavior analysis of travelers and over the discrete-time intervals for a day. Comparison studies based on the Sioux Falls network reveal that TSC with a multistage optimization strategy will drop 8.79% from that with a one-time optimization strategy. Charging service quality, relocation cost, and road network scales have a significant impact on the optimization results according to the sensitivity analysis.

An economic evaluation of teledermatology care delivery for chronic skin diseases.

Aim: Analyze the impact of nationwide implementation of teledermatological care for psoriasis. Methods: Develop a Markov model that estimates theimpact of telehealth technology for treatment of moderate-to-severe psoriasis on health and healthcare expenditures compared with in-person clinical care. Results: Lower medical costs by US$1.5 billion and total social costs of US$4.3 billion over 5 years. Patients save more than 67 million hours in work absenteeism and travel time, valued at US$598 million. Employers save US$1.2 billion over 5years due to decreased employee absenteeism. Conclusion: National implementation of telehealth for psoriasis care has the potential to substantially reduce both formal healthcare costs and informal costs for families and patients, while maintaining equivalent clinical outcomes as traditional in-person care.

Assessment of environmental and economic benefits of packaging waste system in Italy

The paper quantifies social costs and social benefits of packaging waste management system in Italy from 2015 to 2030. Future social costs and benefits are estimated based on the growth of waste generated and on the stringency of recycling and landfilling targets. Packaging waste generation is projected based on macroeconomic factors, consumer practices and technological eco-innovations. Social benefits are derived by the reduction of externalities' correspondent to the achievement of packaging waste management targets. We quantify two environmental ben-efits: avoided emissions and raw materials saved. We find that social costs may rise substan-tially in the future, as total system's operations costs in 2030 may increase by 48% with re-spect to 2015, due to the growth in the quantities of packaging waste generated. Meeting recy-cling and landfilling targets set by the new Circular Economy Package would imply a further 5% increase in total social costs in 2015. This latter increase is more than compensated by so-cial benefits related to the reduction in externalities due to more stringent targets.