Policy Simulations Research Articles

Tripartite Evolutionary Game and Policy Simulation: Strategic Governance in the Redevelopment of the Urban Village in Guangzhou

The scarcity of land drives urban village redevelopment projects, which involve interest redistribution among stakeholders with distinct demands. This paper utilizes evolutionary game theory and simulation methods, constructing a tripartite game model under the institutional arrangement of bottom-up with private developer funding. This study identifies the stable strategies and evolutionary trends of the tripartite interactions under four distinct scenarios and validates these strategies through simulations. The redevelopment of XC village validates the assumptions of the model and theoretical analysis, suggesting that when private developers adopt forced demolition strategies, although villagers ultimately choose to sign the contract of property exchange, it can easily lead to social conflicts. These research findings can enlighten the government to form a tripartite alliance to smooth urban village redevelopment.

Coupling and coordination of food security and agricultural carbon emission efficiency: Changing trends, influencing factors, and different government priority scenarios

Seeking a balance between food security and carbon mitigation is key to achieving sustainable agricultural development. This study evaluates the coupling coordination degree (CCD) between the food security index (FSI) and agricultural carbon emission efficiency (ACEE) in China from 2010 to 2021 using the coupled coordination model. By adjusting the model coefficients, different government priority scenarios are simulated to explore their impact on CCD. The Geodetector method is employed to identify the influencing factors of CCD, investigate their interactions, and assess the differences in these factors across various government priority settings. The average CCD between FSI and ACEE exhibits a notable upward trend, rising from 0.4583 in 2010 to 0.6595 in 2021. Furthermore, regional disparities are widening, particularly in the major production areas. Catch-up effects exist within regions. Policy simulations showed staged interactions between food security and agricultural carbon efficiency, shifting from food security to balanced production and ecology, then to prioritizing low-carbon production for food security. Adjusting policy priorities can effectively improve coupling coordination in the short term, with increasing impact as priority shifts. CCD is influenced by policy, technology, economy, and society, varying with policy priorities. In the baseline scenario, key factors for CCD include the urban-rural income gap, technological advancement, urbanization, and farmers' education level. When the government prioritizes food security, the impact of narrowing income gaps and agricultural industry agglomeration becomes more pronounced. Conversely, emphasizing carbon emission efficiency enhances the influence of technological advancements and urbanization on CCD. Tailoring agricultural production strategies to local conditions and emphasizing interactive effects among factors is crucial for achieving environmentally friendly and high-quality agricultural development goals.

Regional energy consumption policy practice to promote low-carbon economy

IntroductionEnergy is the fundamental driving force for world economic and social development. However, the growth of carbon dioxide emissions and increasing environmental challenges are becoming increasingly problematic, limiting the pace of economic development.MethodsTo optimize the energy consumption structure and promote the development of low-carbon economy, this study took Shaanxi Province as an example to analyze the impact of low-carbon economy policy variables on the energy consumption structure dominated by coal. The purpose of this study is to examine the influence of policy variables on the evolution of energy consumption structure by constructing a system dynamics model. In addition, this study will explore policy optimization issues.ResultsThe error of simulation results was within ±10%. The consumption of non-fossil energy increased from 2,196,400 tons of standard coal in 2009 to 22.578 million tons in 2030, which is in line with the development trend. Under these four policy combinations, the share of coal will fall to 57.8 percent by 2030. In addition, the share of non-fossil energy sources will increase to 21 percent and the share of natural gas will reach 16 percent.DiscussionThe policy suggestions put forward in this paper are of great significance to the optimization of energy structure led by coal. The model constructed can be effectively applied to practical policy simulation and adjustment, and can provide reference for the development of low-carbon economic policies in other regions.

Bracing urban governance against climate crises: How to integrate high reliability into strategic decision‐making?

AbstractTurning cities socio‐ecologically resilient is one of the pressing challenges in the face of ongoing climate change and accompanying extreme weather events. Despite concentrated efforts within specific urban domains, there exists a necessity for a coordinating mechanism that can concurrently monitor signals of slowly maturing long‐term crises, such as global warming and swiftly intervene to mitigate urgent threats, such as catastrophic floods. This article explores the potential of high reliability principles to inform such a governance mechanism through a novel policy operations room (POR) framework, conceptualizing urban strategic goals as critical infrastructure. We analyze the conditions for integrating elements from top‐down crisis management with bottom‐up participatory scenario exercises and policy simulations in urban governance. Based on pre‐ and post‐POR interviews and meeting transcripts in three Finnish cities we examine the regulative, normative, and cognitive aspects of integration. The analysis indicates that the implementation of a high reliability framework in urban settings can provide valuable support for addressing challenges induced by climate change.

Computational model for policy simulation and prediction of the regulatory impact of front-of-package food labels

Non-communicable diseases (NCDs), a significant cause of global mortality, necessitate targeted policy measures to reduce exposure to risk factors, particularly nutrition-related ones. Front-of-pack food labeling aims to shape consumer behavior and decrease exposure to nutritional risk factors. There are various forms of this policy, including traffic light (TL), nutrient profile model (NPM), and reference value (RV) labels. Mathematical models incorporating experimental and dose–response data support determining the most effective form. When selecting a modeling method, it is essential to consider the uncertainty involved in estimating risk factors. This study aims to create a computational model to predict the health and economic impacts of different policy options using an improved version of the Scarborough algorithm, incorporating a validation process to address potential uncertainties. The model shows that NPM was the most effective, reducing the estimated annual mortality rate from 514,247 cases to 447,394 cases (13% reduction). The following labels were TL (11% reduction) and RV (6% reduction). Economically, the RV label affects consumer spending the least, predicting an 8% increase in monthly spending per individual from the baseline. When assessed from both health and economic perspectives, the RV label has emerged as the most advantageous front-of-pack food labeling policy. The proposed model can aid policymakers in evaluating policy alternatives and thus selecting the most advantageous policies based on health and economic outcomes while ensuring the model's accuracy and applicability for the community.

Economic Growth Model with Fertility Incentives: A Theoretical Analysis Framework and Policy Simulation for China

This study presents an overlapping generation model to examine the impact of fertility incentive policies on economic growth in China, incorporating endogenous fertility, education, and technological advancement by integrating unified growth theory with an R&D-based growth model. We analyze the impact of fertility incentive policies on economic growth by evaluating maternity insurance and public education. The results show that increasing the actual contribution rate of maternity insurance, maternity benefits, and public education expenditures can boost China’s fertility rate; nevertheless, extending maternity leave would not incentivize an increase in the fertility rate. The impact of fertility incentives on economic growth is ambiguous, depending upon the extent to which increased fertility dilutes aggregate human capital via technical advancement. If the dilution effect is weak, the sign will turn positive, and vice versa. The simulation analysis of the benchmark model indicates that the fertility incentive policy, which encompasses increasing the actual contribution rate of maternity insurance, maternity benefits, and public education expenditure, can foster economic growth in China by enhancing the fertility rate in the long term.

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.

Effects of autism spectrum disorder on parents labour market: Productivity loss and policy evaluation in Kazakhstan.

Kazakhstan has witnessed a significant increase in the number of Autism Spectrum Disorder cases due to the implementation of mechanisms for early detection. However, despite these efforts, accessing quality services and effective interventions for individuals with Autism Spectrum Disorder remains challenging. While the government has implemented various policies to address the impact of Autism Spectrum Disorder on the labour market, especially for those with disabilities, the effectiveness of these policies needs to be evaluated. Therefore, this article aims to estimate the loss of productivity by calculating the cost of non-working for parents of children with Autism Spectrum Disorder. To achieve this goal, we combine data from official sources with data from our own survey to estimate the loss of productivity using human capital models. In addition, we conduct policy simulations to assess the impact of the existing policy implemented in Kazakhstan that recognises the time of caring for children with Autism Spectrum Disorder and disability as working in the paid labour market. Our results reveal that the productivity loss is substantial, with mothers of children with Autism Spectrum Disorder being particularly affected. Furthermore, based on the outcomes of the policy simulations, it becomes evident that policies solely targeting parents of children with Autism Spectrum Disorder and disability are insufficient to address the labour market gaps and the consequent loss of productivity. To effectively mitigate the impact of Autism Spectrum Disorder in the labour market, a more comprehensive approach is needed. This approach should encompass a broader range of interventions and support mechanisms, including those for individuals without disabilities and parents of children with Autism Spectrum Disorder.

Features of extreme pollution and its influencing factors: evidence from China.

Extreme pollution has become a significant environmental problem in China in recent years, which is hazardous to human health and daily life. Noticing the importance of investigating the causes of extreme pollution, this paper classifies cities across China into eight categories (four groups plus two scenarios) based on the generalized extreme value (GEV) distribution using hourly station-level concentration data, and a series of multi-choice models are employed to assess the probabilities that cities fall into different categories. Various factors such as precursor pollutants and socio-economic factors are considered after controlling for meteorological conditions in each model. It turns out that concentration, concentration, and population density are the top three factors contributing most to the log ratios. Moreover, in both left- and right-skewed cases, the influence of a one-unit increase of concentration on the relative probability of cities falling into different groups shows an increasing trend, while those of concentration show a decreasing trend. At the same time, the higher the extreme pollution level, the bigger the effect of and concentrations on the probability of cities falling into normalized scenarios. The multivariate logit model is used for prediction and policy simulations. In summary, by analyzing the influences of various factors and the heterogeneity of their influence patterns, this paper provides valuable insights in formulating effective emission reduction policies.

Cannabis consumers’ preferences for legal and illegal cannabis: evidence from a discrete choice experiment

BackgroundIn U.S. states that legalized and commercialized recreational cannabis, cannabis sales in illegal markets are still sizable or even larger than those in legal markets. This study aimed to assess cannabis consumers’ preferences for purchasing cannabis from legal and illegal markets and estimate the trade-offs under various policy scenarios.Methods963 adults were recruited, who used cannabis in the past year and lived in a state with recreational cannabis legalization. In a discrete choice experiment, participants chose purchasing cannabis from a legal dispensary or an illegal dealer with varying levels in product attributes including quality, safety, accessibility, potency, and price. Mixed logit models were used to analyze preferences.ResultsThe likelihood of choosing legal cannabis increased with a higher quality, the presence of lab test, a shorter distance to seller, a higher tetrahydrocannabinol level, and a lower price. The likelihood of choosing illegal cannabis increased with a higher quality, a shorter distance to seller, and a lower price. Among product attributes, quality and accessibility were perceived to be the most important for legal cannabis and price was perceived to be the most important for illegal cannabis. Policy simulations predicted that improving quality, ensuring safety, allowing delivery services, increasing dispensary density, and lowering prices/taxes of legal cannabis may reduce illegal cannabis market share.ConclusionsIn the U.S., cannabis consumers’ preferences for illegal cannabis were associated with both legal and illegal cannabis product attributes. Policies regulating legal cannabis markets should consider potential spillover effects to illegal markets.

Participation in Multidisciplinary Teams Among Healthcare Professionals: A Discrete Choice Experiment in Tertiary Public Hospitals in China.

Healthcare professionals' participation is crucial for the efficient implementation of multidisciplinary team (MDT) collaboration models. We identified the key factors influencing healthcare professionals' preference to participate in MDTs in tertiary hospitals. To clarify the attributes and levels of the discrete choice experiment (DCE), we conducted a targeted literature review and conducted in-depth interviews with MDT service providers. Following this, a DCE was designed to evaluate healthcare professionals' preferences for MDT participation, and the influence of factors such as salary subsidies, leadership attention, patient participation, quality assessment, working intensity, and case complexity. A conditional logit model estimated the utility of each attribute. Willingness-to-pay estimates were derived by taking the negative ratio of the coefficients of non-economic and economic attributes. A series of policy simulation analyses were conducted. Two hundred healthcare professionals completed the questionnaire, with 180 valid responses used for analysis. All attributes were statistically significant. Leadership attention and working intensity were the primary factors influencing staff willingness to participate in MDTs, followed by quality assessment and salary subsidies. Significant preference differences were observed between respondents; compared with mid-level staff, senior-level healthcare professionals believed patient engagement would be more helpful in boosting participation. The policy simulation showed that changing leadership attention from "neglect" to "emphasis" would increase the probability of staff choosing to participate in MDTs from 24.4% to 66.98%. Leadership attention was the primary concern for healthcare professionals in MDTs. To effectively motivate staff participation in MDTs, policymakers should adopt a holistic approach that considers work motivation and individual backgrounds, including competitive salary packages and a positive work environment. They should concurrently introduce MDT case complexity measurement tools to optimize resource allocation. Addressing staff members' unique needs and career aspirations by creating targeted training programs, pathways for advancement, and personalized career development plans are also crucial.

Drivers of Healthy Lifestyle Behaviors among Arable Crop Farmers: Evidence from Kwara State, Nigeria

Globally, a healthy lifestyle is important in maintaining the balance between man and the environment as well as the prevention and avoidance of Non-Communicable Diseases (NCDs) especially among rural dwellers in developing economies where the state of quality healthcare is in chaos. In theory, individuals are products of investments made in their health which are either health-promoting or health-damaging behavior. We causally identify the motives of healthy lifestyle practices among arable crop farmers in Kwara state, Nigeria using a cross-sectional survey. We found out that less than average of the arable crop farmers sampled practiced healthy lifestyles and the factors that strongly influenced the healthy lifestyle behavior of arable crop farmers were gender, age, education, farm size, farm income as well as distance to healthcare. A policy simulation illustrates that taking consideration of the socioeconomic characteristics that drive the practice of healthy lifestyles will improve the health status of the populace, especially the rural dwellers, and also avoid lifestyle-related diseases in the future.

Simulation of policies to promote pro‐environmental behavior in a dynamic learning model of social networks: Cases of Brazil, the United States, Japan, Germany, China, and India

AbstractPro‐environmental behavior does not seem to diffuse sufficiently in Brazil, China, Germany, Japan, India, or the United States. Based on the current distribution of carbon footprints and learning patterns (DeGroot learning, best practice, and free riding) in social networks in the six countries, what will happen to the diffusion of pro‐environmental behaviors in each country? What policy measures will be most effective? This study aims to simulate the diffusion of pro‐environmental behavior and to test the effectiveness of various policy measures in a dynamic social network model in the six countries. The distribution of pro‐environmental behaviors is generated by global gridded carbon footprint data. The distribution of learning patterns in social networks is estimated in each country, using data from the World Values Survey. This study considers three policy measures: increasing the number of people pursuing best pro‐environmental behavior practices (best practice promotion, [BPP]), decreasing the number of free riders (free‐riding reduction, [FRR]), and converting the learning patterns of people with high degrees of centrality into best practice (centrality‐based best practice promotion, [CBPP]). Interesting findings were threefold. First, there are large differences in the estimated degrees of individual pro‐environmental behavior and the proportions of learning patterns in the six countries. Second, FRR and BPP are effective in promoting pro‐environmental behavior, but the choice of FRR or BPP depends on the initial proportion of free riders. Third, CBPP is the most effective policy of the three in all countries. Centrality in social networks must be considered when implementing intervention.

Measuring gendered values of time for married couples by life stage based on an intertemporal household utility-maximization model

We investigate the value of time as a resource (VOTR) and the value of childcare time saving (VOCTS) for a married couple with children by life cycle stage. Extending the framework of DeSerpa (1971), we develop a novel intertemporal utility-maximization model that can represent trade-offs within an individual and within a couple between different activities in their life stages based on a household lifetime equilibrium, and we derive wives’ and husbands’ time values when their first child is of pre-school age and after their first child reaches school age. Applying the model to the 2004–2018 Japan Household Panel Survey, we analyze couples in two life stages to empirically find the value of time by gender. The results show that the wives’ average VOTR is greater than 4400 yen/hour with statistical significance when their first child is of pre-school age; the value, however, drastically drops to around 400 yen/hour with statistical insignificance after their first child reaches school age. Conversely, the magnitudes of the husbands’ VOTRs do not change much in different life stages. In the background mechanisms, the wives’ high and low VOTRs reflect their short and long work and commute hours, respectively, whereas the husbands reduce their work and commute hours only slightly over time. For the dual-income households that only spend the minimum required time on childcare, VOCTS is statistically insignificant when their first child is of pre-school age but is greater than 28,000 yen/hour after their first child reaches school age. Using the estimated time values for urban and transport policy simulations, we find that enabling work flexibility could help households increase welfare more compared to transportation improvement and childcare support services.

The division of parental leave: Empirical evidence and policy design

We study several key aspects of the design of parental leave systems. First, we estimate parents’ willingness to pay for parental leave using Danish administrative data on the division of leave from almost 190,000 births combined with sharp variation in economic incentives created by the parental leave benefit system. We find evidence of both strong behavioral responses with significant bunching at kink points and a willingness to pay for a gender-traditional allocation of leave, where fathers take little or no leave. Second, we provide a menu of counterfactual policy simulations showing substantial interaction effects between earmarked leave, replacement rates and the duration of leave benefits. Relevant for the implementation of a recent EU directive, a higher replacement rate significantly increases the behavioral response of fathers to earmarked leave. Finally, we discuss the welfare effects of different policies aimed at increasing the parental leave of fathers.

A hybrid system dynamics model for power mix trajectory simulation in liberalized electricity markets considering carbon and capacity policy

Policy design during the energy system transition requires systematic thinking to navigate its complexity and mitigate potential side effects that policies may induce. Policy design based on System Dynamics (SD) simulations offers a tool to analyze real-world complexities. Given the difficulty of strictly simulating grid dispatch through causal loops, the Screening Curve (SC) method is widely employed in SD policy simulation models within closed commercial software platforms. Nevertheless, as Variable Renewable Energy (VRE) rises, the SC method faces challenges in accurately simulating grid dispatch operation due to its oversight of the power plants' limited output adjustment capabilities. This study developed a hybrid SD model as an alternative to the typical SD-SC combined method, allowing the SD model to simulate the power mix trajectory change incorporating high VRE penetration. After examining the simulation results with Hokkaido as a case study, the SC method may overestimate the abatement outcomes of the carbon pricing, and underestimate the support from capacity mechanism for the coal power plants. The difference between the two model increases along with the VRE share rising, when the VRE share exceeds about 40 % the results of the hybrid model will be more reliable.

Wage subsidy in the DRC: A CGE analysis

This paper analyzes the impact of wage subsidies on lower-skilled formal workers in the Democratic Republic of Congo (DRC). It employs a multi-sectoral, empirically-calibrated general equilibrium model to capture the economy-wide transactions between the formal and informal sectors and assess policy simulations in the DRC. The simulations, both in the short and long run, indicate that when the government provides wage subsidies to lower-skilled workers, it significantly improves the real disposable incomes of both formal and informal households. There is a general increase across formal and informal sectors in real household disposable incomes due to the wage subsidy. The results show that subsidy allocation narrows the income gap between high and low-income households, as well as between formal and informal sectors. The findings are insightful for wage policy simulations, as the wage subsidy targeting lower-skilled formal workers increases real GDP from the expenditure side by 1.19% and 3.19% in the short and long run, respectively, from the baseline economy.

The projected impact of the inflation reduction act's climate provisions on cardiovascular and respiratory outcomes

OnjectiveClimate change and environmental pollution have known health effects. The recently introduced inflation reduction act (IRA) by the United States government includes funding initiatives to curb climate change, and reduce environmental pollution, in line with the nationally determined contribution (NDC) plan (40–50 % reduction in greenhouse gas [GHG] emissions by 2030, as compared with 2005). The projected cardiovascular health benefits of the IRA driven climate actions to achieve the NDC goals are not known. MethodsWe used the Energy Policy Simulator (EPS), a simulation algorithm based on systems dynamics modelling estimating the impact of various energy policies, to model the impact of achieving NDC targets in the United States on health outcomes by 2050. We further investigated race-specific impact on mortality (absolute and relative) by 2050.We estimated the projected reduction in six adverse health outcomes between 2022 and 2050: asthma attacks, non-fatal heart attacks, hospital admissions, respiratory symptoms and bronchitis, lost workdays, and deaths. ResultsAchievement of NDC targets by 2050 will result in 987,415 avoided asthma attacks, 41,565 avoided nonfatal heart attacks, 18,993 avoided hospital admissions, 1,493,010 avoided respiratory symptoms and bronchitis, 3,317,250 avoided lost workdays, and 32,659 avoided deaths (22,839 among white individuals, 4993 among Black individuals, 2801 among Asian individuals, and 2026 among other/multirace individuals). By 2050, minority racial groups had higher relative change in avoided deaths (white -0.74 %, Black -1.01 %, Asian -1.24 %, and other/multirace -1.75 %). Similarly, Hispanics/latinos higher relative reductions in deaths (-1.4 %) compared with non-Hispanic/Latinos (-0.7 %) by 2050. ConclusionThe IRA facilitated achievement of NDC GHG reduction goals by 2050 would result in substantial number of avoided adverse health outcomes and death. Racial and ethnic minorities are expected to have the largest relative reductions in deaths by 2050. The current report underscores the importance of continued climate action investment irrespective of political differences. The appreciation of this aspect of the IRA may be more important to overall preservation of health, beyond the reduction in medication costs.

The impact of energy prices on industrial investment location: Evidence from global firm level data

This study examines the influence of relative energy prices on the geographical distribution of industrial investments across 41 countries. Employing a gravity model framework to analyse firms’ investment location decisions, we estimate the model using global bilateral investment flows derived from firm-level M&A data. Our findings reveal that a 10% increase in the energy price differential between two countries results in a 3.2% rise in cross-border acquisitions. This effect is most pronounced in energy-intensive industries and transactions targeting emerging economies. Furthermore, policy simulations suggest that the impact of unilateral carbon pricing on cross-border investments is modest.

International spillovers of conventional versus new monetary policy

We use a calibrated open-economy New Keynesian model to compare the spillovers from conventional and new monetary policy shocks from the US to Canada. We find that shocks with similar effects on the US GDP affect Canada’s private bond yield differently. For example, a forward guidance shock increases Canada’s private bond yield by 0.15%, however a quantitative easing (QE) shock decreases it by 0.13%. Our counterfactual policy simulations for the 2008 crisis show that (1) a more aggressive QE by the Fed would have resulted in a milder recession in the US but a steeper one in Canada. (2) If the Bank of Canada had followed the Fed’s QE policies, the drop in Canada’s GDP would have been much smaller. (3) Finally, if the Fed had implemented a mild negative interest rate policy, the effects on the Canadian economy would be similar to those of the benchmark zero-rate policy.