Impact Of Different Policies Research Articles

Fit for 2030? Possible scenarios of road transport demand, energy consumption and greenhouse gas emissions for Italy

Decarbonizing the transportation sector is one of the key challenges to reduce greenhouse gas (GHG) emissions and meet EU Green deal 2030 targets. Road transport decarbonization requires a holistic approach encompassing various strategies and interventions aimed at fostering the sustainable transition of this sector. This challenge is crucial given that road is responsible for over 90% of the overall transport GHG emissions.Within this framework, this study aims, for the Italian case, to: a) estimate the national passenger and freight road travel demand with respect to three different time periods: 2005 (reference year for EU “fit for 55” target); 2019 (last consolidated pre-covid year); 2022 (base-line year); b) define possible road transport scenarios to 2030 following the Avoid-Shift-Improve (ASI) approach; c) estimate the GHG and energy consumption inventory related to national road transport for the 2005, 2019; 2022 and 2030 scenarios. Due to the current “deep uncertainty” regarding key economic and social variables in this period, two forecasting scenarios – named “moderate decarbo” and “high decarbo” – were designed and based on different and somewhat opposite hypotheses on exogenous variables (e.g., GDP) and penetration rates of measures and policies promoting sustainable transportation (e.g., subsidies for electric vehicles) already in place. Although the current European legislation refers only to Tank-to-Wheel (TtW) objectives, this study included also the Well-to-Wheel (WtW) evaluation, which is more accurate in assessing the GHG impact of different policies, energy carriers, and technologies. As for the past, results suggest that GHG reductions over the period 2005–2022 are mostly due to crises and stagnation of the Italian economy. “Moderate decarbo” forecasts for 2030 lead to a 12% reduction of TtW (and 15% for WtW) GHG emissions compared to 2005 levels, with ASI policies compensating for the assumed economic growth. In any case these values are far from the EU “Fit for 55” goal setting a 43.7% reduction of TtW emissions. Even under very favourable hypotheses (“high decarbo” scenario), GHG emissions might be reduced by only 28% for TtW and 33% for WtW, still well below EU objectives. Furthermore, passengers and freight show very different behaviour, with the latter contributing to emissions more than proportionally, and “shift-to-rail” policies, although very useful in some market segments, contribute moderately at the overall national level. The main conclusion of this study is that the pathway towards road transport decarbonization in Italy (and possibly in other EU Countries) is very uncertain, and the ambitious 2030 target is unlikely to be met with currently planned policies alone. The transportation system needs to be constantly monitored, and new policies have to be implemented to reach higher emissions reduction goals.

The Effects of Admission Policies on Rates of Family Migration in European States, 2008–2019

This study investigates whether and how much family migration policies have influenced patterns of family migration in European states. Family migration has been the largest category of entry in many Western countries in recent decades, and it has been the subject of increasing contestation in political debates. While research on family migration policies has advanced in recent years, no comprehensive cross-country comparisons have been done of the impact of different policies on the size and composition of family migrant inflows. This study addresses this gap by analysing the connection between admission policies and rates of family migration in 31 European states during the 2008 to 2019 period. Combining data from Eurostat and the Migrant Integration Policy Index (MIPEX), this study uses a time-series regression analysis to assess the effects of admission policies on different types of family related immigration. While restrictive admission policies have led to falling overall levels of family migration, the analysis here reveals stratifying implications, whereby the effect has been greater where the sponsor is a non-EU citizen than where he/she is an EU citizen. By providing evidence on the differential impacts of admission policies on family related immigration, this study contributes new insights on the effects of restrictive migration policies.

Have mixed traffic restrictions improved air quality in different regions?

Governments have implemented many regulations to solve traffic congestion and pollutant emissions. Traffic restriction (TR) policy is the most common and widely used traffic regulation. However, it is difficult to alleviate traffic pollution through TR policies. Only a few studies focus on TR policies in multiple cities. In this paper, we classify traffic restrictions by geographical factors and evaluate the impact of different policies based on PSM-DID first. Then, we classify the mixed restrictions and test their effects on air pollution. The results show that TR policy improves air quality regardless of the season. Moreover, the effect of TR policy is not affected by the superposition of the type of traffic restriction. For cities implementing the same TR policy, geographic factors can lead to different outcomes. This finding provides a reference for governments in reducing air pollution.

Modeling the Impact of Different Policies on Electric Vehicle Adoption: An Investigative Study

Electric Vehicles (EVs) emerge as a crucial solution for alleviating the environmental footprint of the transportation sector. However, fostering their widespread adoption demands effective, targeted policies. This study introduces a versatile model, amalgamating stakeholders and policies and leveraging local data with broader market applicability. It delineates two key EV adopter groups—innovators and imitators—shedding light on their evolving impact on adoption trends. A pivotal feature of the model is the factoring of EV attractiveness, comprising Life-Cycle Cost (LCC), Driving Range, Charging Time, and infrastructure availability, all of which are expected to improve with the fast technological advancement of EVs. Financial policies, notably subsidies, prove potent in boosting EV adoption but fall short of targeted sales due to imitator lag. In response, a pragmatic solution is proposed: a government-led EV acquisition of 840 EVs, coupled with a 20% subsidy on new EV purchases and a 20% tax on new ICEV purchases, potentially realizing a 30% EV sales target by 2035. Future research avenues may delve into behavioral dynamics prompting imitators’ adoption, optimizing EV infrastructure strategies, and assessing the socio-economic impacts of EVs. Interdisciplinary approaches hold promise for enriched insights for effective EV integration policies.

Feed-in tariff or sheep farming subsidy? Implications of promoting photovoltaic energy by solar sheep raising

This paper theoretically establishes a solar sheep game model under three cases to capture the friction among the stockholders including the government, PV investors, and shepherds. Nash equilibrium has been revealed for analyzing the impact of different policies on the benefits of each player. The results show that the implementation of the feed-in tariff (FIT) policy can maximize the government’s revenue. However, when the government focuses on promoting poverty alleviation for shepherds, the sheep farming (SF) subsidy policy is more efficient than FIT if the positive effect of the PV panel on sheep raising is not enough. Besides, dynamic system models are established either to study the bounded rationality behavior during the decision process in the long run. Thresholds regarding speed of decision adjusting for each player are found. Results also show the system will lose stability if players adjust their decisions too frequently. Based on data from Talatan and Cascadilla, numerical analysis has been applied to illustrate the managerial implications from the proposed model.

Public acceptance of residential photovoltaic installation: A case study in China

Residential photovoltaics (PV) presents an effective means of achieving low-carbon development, owing to its installation flexibility and resource-saving properties. To explore the residents' behavioral intentions to purchase and install residential PV systems, this study collected 1424 samples and analyze the impact of different policies on residents' adoption of residential PV using the theory of TPB and the Partial Least Squares Structural Equation Model (PLS-SEM). The main conclusions are summarized as follows: (1) Bungalow residents exhibit a stronger inclination to install residential PV than building residents. (2) Perceived reward (PR) affects installation intention (II) more than perceived guide (PG) among bungalow residents. (3) Both PR and PG indirectly affect II through Perceived behavioral control (PBC) among bungalow residents. Economic policies, represented by PR, are likely to have more substantial indirect effects than propaganda and guidance policies. The findings suggest that China's recent residential PV installation policies should increase users' trust and guide the future decline of subsidy policy.

Urban population prediction based on multi-objective lioness optimization algorithm and system dynamics model

Population size is closely related to economic and social development and change. It is one of the primary and essential elements of overall urban development planning to formulate a population development strategy scientifically through population projections. Therefore, we propose an urban population prediction model based on a multi-objective lioness optimization algorithm and system dynamics. The multi-objective lioness optimization algorithm is used to optimize some critical parameters of the system dynamics model to reduce the subjectivity of the model construction. Taking Xi’an as an example, the validity of the model is verified, and the population size of Xi’an from 2019 to 2050 is predicted by the model. In addition, the impact of different policies and their combinations on the future population is discussed through simulations of three scenarios composed of five policy factors: birth, employment, science and technology, healthcare and education. The results show that the total population of Xi’an will peak at 147,939,242 in 2040, based on current development trends. Moreover, the five policies with the largest to smallest positive effect on population size are: employment policy, fertility policy, education policy, science and technology policy, and health policy, with employment and fertility policies having significantly larger effects than the other three. Therefore, the employment policy and the birth policy are the two most effective policies to promote population growth, and the coordinated implementation of the five policies is the fastest way to increase population size.

Dynamic simulation of the water-energy-food nexus (WEFN) based on a new nexus in arid zone: A case study in Ningxia, China

‘Quantity-type’ water shortages and ‘quality-type’ water shortages are important factors that constrain the security and coordinated development of regional water-energy-food nexus (WEFN) system, especially in arid areas where water is scarce. Therefore, it is of great significance to accurately identify the coupling and mutual feedback effects of the WEFN on different types of water resources demand. This information can be used to understand the regional WEFN and alleviate pressure on regional water resources. In this study, a new relationship diagram of the WEFN system was formed with the incorporation of the water footprint (WF), which can characterize a system's water demand, water source type, water pollution amount and pollution type. Based on this nexus, a WEFN system feedback model suitable for arid regions was established using the system dynamics approach. Taking Ningxia, China, as a case study, six future scenarios were designed, and the development trends of the WEFN system under different development scenarios were simulated to explore the impact of different policies on the WEFN. Finally, practical suggestions to promote the synergistic development of WEFN systems were proposed. The results indicate that the rational distinction between ‘quantity-type’ and ‘quality-type’ water resources can effectively alleviate the regional water stress and promote the coordinated development of water, energy and food. And the water security is the main factor that constrains the coordinated development of the WEFN system in Ningxia. Incorporating water environmental pollution policies into the water subsystem is necessary. Furthermore, the resource saving scenario and energy production restructuring adjustment scenario can effectively alleviate the energy security problems that has resulted from rapid economic development. In addition, Ningxia urgently needs to optimize its food production structure to address a continuous reduction in the food security index.

Analysis The Policies Balancing Conservation and Community Interests in Maasai Mara

This paper aims to examine what policies are appropriate to balance conservation and community interests in the Maasai Mara. Grey Relational Analysis (GRA) is used to examine the relationship between indicators divided into two categories: wildlife species interests and economic industrial interests. A qualitative analysis is performed to establish a one-to-one linear correlation between the indicators. Multiple objective nonlinear programming and genetic algorithm are then applied to determine an objective function that quantizes the interests. Maximize the number of wildlife species and local community income by simulation. After the analysis of that simulation, an Fuzzy Comprehensive Evaluation (FCE) model combined with the Analytic Hierarchy Process (AHP) are employed to analyze the impact of different policies for different regions by studying the relationships between factors affecting the wildlife reserve, such as migration patterns of wildlife and the impact of human activities. This approach helps to determine the best policy decision based on the scores and pros and cons of each policy.

The Impact of Government Behavior on the Development of Cross-Border E-Commerce B2B Export Trading Enterprises Based on Evolutionary Game in the Context of “Dual-Cycle” Policy

In 2020, the COVID-19 pandemic had a major impact on China’s foreign trade. Therefore, the Chinese government has proposed a “dual cycle” policy to promote economic development. In 2021, China’s cross-border e-commerce B2B exports accounted for 60 percent. Therefore, this paper studies the impact of government actions on the development of cross-border e-commerce B2B export enterprises under the background of “dual cycle” policy. First, the policies related to the cross-border e-commerce industry in the “dual circulation” policy are screened, and the LDA topic model is used to classify them, i.e., sorting by topic intensity as “fiscal policy”, “tax policy”, “customs clearance policy”, “payment policy” and “talent policy”. After that, based on the analysis results of the LDA topic model, a theoretical basis for the impact of different policies on cross-border e-commerce B2B export companies is established; then an evolutionary game model between the government and cross-border e-commerce B2B export enterprises is constructed. This article also carried out experiments to verify our analysis. The simulation results show that: (1) The government’s appropriate increase in subsidies, tax incentives, infrastructure investment, talent introduction and cultivation, optimized payment system, and supervision can promote enterprises to participate in cross-border e-commerce B2B export trading; (2) excessive government supervision reduces enterprises’ enthusiasm to participate in cross-border e-commerce B2B export trading; (3) the government’s subsidies, tax incentives, and supervision strength have the greatest impact on whether enterprises participate in cross-border e-commerce B2B export trading, followed by the government’s investment in cross-border e-commerce infrastructure, the introduction and cultivation of cross-border e-commerce talents, and the improvement of the payment system. Finally, this paper puts forward relevant policy recommendations to promote the development of cross-border e-commerce B2B export enterprises.

Big data analytics and the effects of government restrictions and prohibitions in the COVID-19 pandemic on emergency department sustainable operations.

Grounded in dynamic capabilities, this study mainly aims to model emergency departments' (EDs) sustainable operations in the current situation caused by the COVID-19 pandemic by using emerging big data analytics (BDA) technologies. Since government may impose some restrictions and prohibitions in coping with emergencies to protect the functioning of EDs, it also aims to investigate how such policies affect ED operations. The proposed model is designed by collecting big data from multiple sources and implementing BDA to transform it into action for providing efficient responses to emergencies. The model is validated in modeling the daily number of patients, the average daily length of stay (LOS), and daily numbers of laboratory tests and radiologic imaging tests ordered. It is applied in a case study representing a large-scale ED. The data set covers a seven-month period which collectively means the periods before COVID-19 and during COVID-19, and includes data from 238,152 patients. Comparing statistics on daily patient volumes, average LOS, and resource usage, both before and during the COVID-19 pandemic, we found that patient characteristics and demographics changed in COVID-19. While 18.92% and 27.22% of the patients required laboratory and radiologic imaging tests before-COVID-19 study period, these percentages were increased to 31.52% and 39.46% during-COVID-19 study period. By analyzing the effects of policy-based variables in the model, we concluded that policies might cause sharp decreases in patient volumes. While the total number of patients arriving before-COVID-19 was 158,347, it decreased to 79,805 during-COVID-19. On the other hand, while the average daily LOS was 117.53 min before-COVID-19, this value was calculated to be 165,03 min during-COVID-19 study period. We finally showed that the model had a prediction accuracy of between 80 to 95%. While proposing an efficient model for sustainable operations management in EDs for dynamically changing environments caused by emergencies, it empirically investigates the impact of different policies on ED operations.

The impact of different incentive policies on new energy vehicle demand in China's gigantic cities

License plate control and preferential policies are both significant for promoting New Energy Vehicles (NEVs) in China, and it is important to be aware of the impact of different policies on NEV demand. This study first conducts a survey based on questionnaires answered by 572 residents from Beijing and Shanghai. The conditional-logit model and mixed-logit model are then utilized to analyze the impact of different incentive policies (i.e., auction, lottery and queueing), vehicle attributes, and consumer attributes on China's NEV demand in gigantic cities. The results show that, i) the incentive effect of license-plate auction for purchasing NEVs is better than that of license-plate lotteries and license-plate queueing. ii) there is an evident promotion by the tax exemption policies in Beijing and Shanghai. iii) people hope that the rate of change in subsidies for battery electric vehicles (BEVs) is tied to BEV recharge mileage. iv) among those who have a high demand for purchasing a vehicle, their primary purpose is to commute to work. v) those who have BEV, live in other cities, or have urgent purchasing needs are likely not to choose NEVs. Policy implications have been proposed to promote NEVs in China's gigantic cities.

Recruitment into Organized Crime: An Agent-Based Approach Testing the Impact of Different Policies

Recruitment into Organized Crime: An Agent-Based Approach Testing the Impact of Different Policies

Diffusion and benefits evaluation of electric vehicles under policy interventions based on a multiagent system dynamics model

Electric vehicle (EV) diffusion plays a vital role in transforming and upgrading China's automobile industry by ensuring energy security, improving air quality, and contributing to achieving the goal of carbon neutrality by 2060. EV diffusion is a complex dynamic system that involves various agent interactions. Nevertheless, previous research has rarely focused on the impact of different policies on EV diffusion under multiagent interactions and thus has not systematically evaluated its diffusion benefits. This paper establishes a multiagent system dynamics (SD) model to explore the impact of different policies on the diffusion and benefits of EVs. The results indicate that (1) EV diffusion is most significantly impacted by licence plate restrictions, following by charging pile construction subsidy, most sensitive to purchase subsidy policies, and least affected by government R&D subsidies. (2) Moreover, all four policies decrease EV energy benefits but positively impact the carbon emission reduction, health, and social benefits of EVs. The health benefits of EVs are the greatest, followed by the carbon emission reduction benefits. (3) The benefits of government subsidies to EV manufacturers, consumers, and charging infrastructure operators are positive. Finally, by integrating the development situation of China's EV industry, feasible policy suggestions are proposed for sustainable development.

Analysis of Concepts Affecting the Public Policies

“Public Policy Analysis” involves in the evaluation of issues of public importance with objective of providing facts and statistics about extent and impact of different policies of Government. Nowadays, public policy analysis is undertaken by scholars from different applied physical and biological sciences (e.g. technology assessments, environmental impact studies, seismic risk analyses, etc.). Presently, the focus is on public policy analysis as it is conducted within the social and behavioural sciences, mainly Political Science, Sociology and Economics. Generally, the public policy addresses real or sensed problems, so the public policy analysis is mostly devoted to defining or clarifying the problems and assessing the needs. Main objective of public policy analysis is to assess the degree to which policies are meeting their aims. Policy analysis plays an important role to define and outline the aims of a proposed policy, and also has role to identify the similarities and differences in expected outcomes and estimated costs with competing the alternative policies. Several public policies are formulated to solve both the current and future problems, and so the policy analysis attempts to forecast future requirements based on present concomitant with the past conditions. A large number of works in the field of public policy analysis involves for the development of the conceptual schemes or typologies which help to sort out different types of policies, or analyses of policies. Many literatures on public policy analysis, and especially on impact evaluation are chiefly methodological in character; indeed, many recent innovations in research procedure have been developed by research scholars working on applied problems. Many texts in ‘evaluation research’ recommend an assessment of the ‘evaluability’ of the programme prior to initiate the evaluation itself. ‘Implementation analysis’ is an essential component of all capable policy evaluations. ‘Utilisation’ is an ongoing problem in the field of evaluation research. Evaluation results affect the public policy by serving as the impetus for public discourse and debate which form social policy, rather than through extensive programme termination or reform.

Impacts of Emission Reduction Technological Changes on China’s City-Level PM2.5 Concentration Based on Sustainable Development

As an important field for human activities, cities play a critical role in PM2.5 reductions. Among the determinants for PM2.5 concentration, technological progress is considered to exhibit significant inhibitory effects. Although most extant research has focused on energy technologies or total factor productivity, due to limitations in data and methods, few scholars have focused on emission reduction technological changes at a city-level scale. Therefore, based on the combination of k-means clustering and the log-mean Divisia index method, this study estimates and explores the impact of PM2.5 emission reduction technology (PME) on the temporal changes and spatial differences of 262 Chinese cities’ PM2.5 concentration during 2003–2017. The findings show the following: (1) although the results based on econometric methods indicate that emission reduction technological changes decreased China’s city-level PM2.5 concentration, there were turning points in the yearly impacts, indicating that the improvements to emission reduction efficiency were not stable; (2) compared with PME, energy intensity played a more stable role in PM2.5 emissions reductions, implying that the improvement of energy efficiency was still very important in controlling PM2.5 concentrations; (3) based on the classified groups after clustering, most cities’ PME contributed to negative differences, but the PME of a small number of cities was very weak to largely lower the average level of their group; and (4) distributions of the spatial decomposition of the three classified groups were stable in the period of 2003–2017, implying that the catch-up and transcendence effects of PME within the group were limited. Thus, policymakers should focus on the impact of different policies on PME differences between cities.

Research on New Energy Vehicle Market Penetration Rate Based on Nested Multinominal Logit Model

This article uses the NMNL (nested multinominal logit) model to analyze the impact of different policies on the cost of owning a vehicle by a consumer and discusses the changes in the share of various fuel-driven types of passenger vehicles that may be brought by different policy portfolios. This article also considers the differences in the development of various technical routes, conducts the nested classification calculation of different models, divides the differences in product preferences and obtains the market share results that are more in line with the market development status, providing a basis for the formulation of policies related to new energy vehicles. The study found that the popularization of NEVs requires more cost-reducing measures. As policies that consumers can perceive, consumers are more sensitive to fiscal and taxation policies than other types of policies. Based on the calculation of policy effects, this article recommends a policy plan to gradually impose vehicle purchase tax on NEVs after 2024, increase the fuel tax rate in stages after 2025, and impose an excise tax on BEVs and FCEVs after 2030. The plan can guarantee the stability of support for NEVs and the gradual reduction of financial investment.

Containment of COVID-19: Simulating the impact of different policies and testing capacities for contact tracing, testing, and isolation.

Efficient contact tracing and testing are fundamental tools to contain the transmission of SARS-CoV-2. We used multi-agent simulations to estimate the daily testing capacity required to find and isolate a number of infected agents sufficient to break the chain of transmission of SARS-CoV-2, so decreasing the risk of new waves of infections. Depending on the non-pharmaceutical mitigation policies in place, the size of secondary infection clusters allowed or the percentage of asymptomatic and paucisymptomatic (i.e., subclinical) infections, we estimated that the daily testing capacity required to contain the disease varies between 0.7 and 9.1 tests per thousand agents in the population. However, we also found that if contact tracing and testing efficacy dropped below 60% (e.g. due to false negatives or reduced tracing capability), the number of new daily infections did not always decrease and could even increase exponentially, irrespective of the testing capacity. Under these conditions, we show that population-level information about geographical distribution and travel behaviour could inform sampling policies to aid a successful containment, while avoiding concerns about government-controlled mass surveillance.

Focusing urban policies on health equity: the role of evidence in stakeholder engagement in an Italian urban setting

ABSTRACT Health equity in All Policies requires decision makers to face the health impact of their decisions and its distribution among different population groups. Since the evidence policy gap often prevents them from using the evidence provided by experts, appropriate mechanisms of governance are needed to overcome this barrier. In Turin (Italy, pop. 900,000) a community of experts, policymakers and stakeholders was engaged in a participatory evidence-based decision-making process to raise awareness and foster intersectoral actions to tackle social health inequalities. This health equity assessment of urban policymaking was based on the action-research model and moved through three steps: i) co-investigation into the mechanisms responsible for generating health inequalities, ii) co-decision of priorities, iii) co-creation and implementation of actions. The two-years process was continuously fed with new evidence from the Turin Longitudinal Study, linking health, resident and socioeconomic stories of individuals and families. Overall, the equity lens is an effective criterion to identify potential health gains as achievable targets for policymaking, a useful metric for comparative analysis of the impact of different policies and a mean to foster stakeholder’ to cooperate. The case-study shows that a systematic cooperative effort with stakeholders is needed to ensure effectiveness and participation in change and innovation.

Recruitment into Organized Crime: An Agent-Based Approach Testing the Impact of Different Policies

ObjectivesWe test the effects of four policy scenarios on recruitment into organized crime. The policy scenarios target (i) organized crime leaders and (ii) facilitators for imprisonment, (iii) provide educational and welfare support to children and their mothers while separating them from organized-crime fathers, and (iv) increase educational and social support to at-risk schoolchildren.MethodsWe developed a novel agent-based model drawing on theories of peer effects (differential association, social learning), social embeddedness of organized crime, and the general theory of crime. Agents are simultaneously embedded in multiple social networks (household, kinship, school, work, friends, and co-offending) and possess heterogeneous individual attributes. Relational and individual attributes determine the probability of offending. Co-offending with organized crime members determines recruitment into the criminal group. All the main parameters are calibrated on data from Palermo or Sicily (Italy). We test the effect of the four policy scenarios against a baseline no-intervention scenario on the number of newly recruited and total organized crime members using Generalized Estimating Equations models.ResultsThe simulations generate realistic outcomes, with relatively stable organized crime membership and crime rates. All simulated policy interventions reduce the total number of members, whereas all but primary socialization reduce newly recruited members. The intensity of the effects, however, varies across dependent variables and models.ConclusionsAgent-based models effectively enable to develop theoretically driven and empirically calibrated simulations of organized crime. The simulations can fill the gaps in evaluation research in the field of organized crime and allow us to test different policies in different environmental contexts.