Effects Of Different Policies Research Articles

Assessing the tradeoffs in emissions, air quality and health benefits from excess power generation due to climate-related policies for the transportation sector

As the transportation sector continues to decarbonize through electrification, there is growing interest in quantifying potential tradeoffs in air pollution and health impacts due to potential excess emissions from the power sector. This study investigates air pollution and health impacts of policy-driven changes in the transportation sector and the associated power generation demand in the Northeast and Mid-Atlantic United States. Five illustrative scenarios were designed to capture the effects of different policies under the first mandatory market-based program to reduce greenhouse gases in the US power sector (Regional Greenhouse Gas Initiative—RGGI) and the Transportation and Climate Initiative (TCI). Considering future power generation with new renewable energy investments to meet demands from decarbonized transportation, the scenarios were framed using: 1. 2030 reference cases for both sectors and a hybrid TCI portfolio, 2. Departure from the reference cases defined by Pennsylvania included or not in RGGI, and 3. Power grid emissions estimated under clean energy standard (CES) policy and hybrid TCI portfolio. While the cross-sectoral policy effect on domain-wide concentrations is modest (max ΔPM2.5 ∼ 0.06 μg m3, ΔNO2 ∼ 0.3 ppbv, ΔO3 ∼ 0.15 ppbv), substantial increases in Ohio and West Virginia were attributed to Pennsylvania joining RGGI. With CES enacted and Pennsylvania in RGGI, significant reductions are seen in average concentrations (max ΔPM2.5 ∼ 1.2 μg m3, ΔNO2 ∼ 1.1 ppbv, ΔO3 ∼ 1.7 ppbv) except for Louisiana and Mississippi with corresponding disbenefits. When focusing exclusively on emissions reductions from transportation, the hybrid TCI portfolio had health benefits of 530 avoided adult deaths, and 46 000 avoided asthma exacerbations. With a ‘business as usual’ power grid, these benefits remain comparable and are mainly driven by NO2, followed by PM2.5 and O3. However, if Pennsylvania joins RGGI, total health benefits and spatial distribution change substantially, with a large portion of adverse health impacts moving from TCI states to Ohio and West Virginia. The overall monetized impact of a CES scenario can substantially exceed the estimated average range of 66–69 Billion US$, depending on the interaction with transportation decarbonization strategies and other drivers of exposure.

State‐Level Immigrant Policies and Ideal Family Size in the United States

AbstractDemographers have long been interested in how fertility ideals vary in response to perceived existential threats. Although migration scholars document the increasingly threatening nature of U.S. immigration policies, little research explores how these policies shape the fertility ideals of those most affected by them. To that end, we exploit spatiotemporal variation in states’ evolving immigrant policy contexts to understand the effects of different policies on the ideal family size of Hispanics—a group who is most likely to be stereotyped as undocumented and most likely to live in mixed‐status households or communities. Specifically, we combine time‐varying information on state‐level immigrant policies with geo‐referenced data from the General Social Survey. Results suggest that ideal family sizes are significantly higher among Hispanics (compared to non‐Hispanic whites) in state‐years with omnibus policies—which bundle multiple restrictive laws together and thus impose sweeping restrictions— compared to state‐years without these policies. On the other hand, sanctuary policies, which aim to curb federal immigration enforcement, and E‐verify mandates, which aim to curb the employment of undocumented immigrants, are not associated with significant differences. Our analyses provide new insights into the complex ways in which the evolving U.S. immigrant policy landscape has far‐reaching impacts on reproductive and family life.

Subsidy policy or dual-credit policy? Evolutionary game analysis of green methanol vehicles promotion

To ensure energy supply, China has developed methanol vehicles since the 1980s. Although methanol vehicles are affordable and technologically mature, their life-cycle emissions are high because China uses coal to produce methanol. China has recently proposed green methanol vehicle (GMV) fueled by methane from CO2 or biomass to reduce emissions. As an essential part of promoting alternative fuel vehicles (AFVs), a policy support scheme is necessary to increase the market share of GMV. To provide a theoretical basis for a policy support scheme before further GMV promotion, this study establishes an evolutionary game model to simulate and compare the effects of different policies on GMV deployment. The results show that by comparing the subsidy policy and the dual-credit policy, the latter is more stable and effective for GMV promotion and application; under the double-points policy, it is more favorable for GMV promotion if the government includes GMV in new energy vehicles.

Elasticities of Passenger Transport Demand on US Intercity Routes: Impact on Public Policies for Sustainability

Passenger transport is a key sector of the economy, and its sustainability depends on achieving the greatest possible efficiency, avoiding problems of congestion or underuse of infrastructures, and reducing the sector’s environmental impact. Knowing the elasticities of demand is critical to achieving these objectives, estimating the intensity of transport demand, and predicting the effect of different policies on reducing greenhouse gas emissions. This research proposes a relatively simple model for estimating and predicting the elasticity of demand for different modes of transport at the route level. This model could be used by companies and public management to obtain a vision of the different analysed routes and the pressure of their demand, as well as a relative perspective of each of them. Such a model is used to estimate the price and income demand elasticities of passenger transport modes in domestic routes in the United States (2003–2019), where there is competition between road, rail, and air transport. Series of passenger numbers, fares, and budget shares are reconstructed from the available data. A Rotterdam demand model (RDM) is estimated using a seemingly unrelated regression method (SUR). The estimated income elasticities imply that demand for road transport increases somewhat more proportionally than the increase in income, somewhat less than proportionally for air transport, and with very low proportionality for rail transport. This indicates the need to target investment and service improvement efforts, as well as technological solutions, according to this difference in demand pressures. Finally, the demand response of the three modes of transport to price increases is inelastic, and there is little or no pass-through from one mode to another. This implies that fiscal or carbon pricing actions could have a very limited impact and high social costs. Again, strategies based on investments in technological progress, infrastructure development, and normative interventions could be more effective.

Referring and Specialist Physician Gender and Specialist Billing

While a gender pay gap in medicine has been well documented, relatively little research has addressed mechanisms that mediate gender differences in referral income for specialists. To examine gender-based disparities in medical and surgical specialist referrals in Ontario, Canada. This cross-sectional study included referrals for specialist care ascertained from Ontario Health Insurance Plan physician billings for fiscal year 2018 to 2019. Participants were specialist physicians who received new patient consultations from April 1, 2018, to March 31, 2019, and the associated referring physicians. Data were analyzed from April 2018 to March 2020, including a 12-month follow-up period. Specialist and referring physician gender (female or male). Revenue per referral was defined based on an episode-of-care approach as total billings for a 12-month period from the initial consultation. Mean total billings for female and male specialists were compared and the differential divided into the portion owing to referral volume vs referral revenue. Difference-in-differences multivariable regression analysis was used to estimate gender-based differences in revenue per referral. For each referring physician, gender-based differences in referral patterns were examined using case-control analysis, in which specialists who received a referral were compared with matched control specialists who did not receive a referral. This analysis considered the gender of the specialist and concordance between the gender of the referring physician and specialist, among other characteristics. Of 7 621 365 new referrals, 32 824 referring physicians, of whom 13 512 (41.2%) were female (mean [SD] age, 46.3 [11.6] years) and 19 312 (58.8%) were male (mean [SD] age, 52.9 [13.5] years), made referrals to 13 582 specialists, of whom 4890 (36.0%) were female (mean [SD] age, 45.6 [11.0] years) and 8692 (64.0%) were male (mean [SD] age, 51.8 [13.0] years). Male specialists received more mean (SD) referrals than did female specialists (633 [666] vs 433 [515]), and the mean (SD) revenue per referral was higher for males ($350 [$474]) compared with females ($316 [$393]). Adjusted analysis demonstrated a -4.7% (95% CI, -4.9% to -4.5%) difference in the revenue per referral between male and female specialists. Multivariable regression analysis found that physicians referred more often to specialists of the same gender (odds ratio, 1.04; 95% CI, 1.03-1.04) but had higher odds of referring to male specialists (odds ratio, 1.10; 95% CI, 1.09-1.11). In this cross-sectional study of the gender pay gap in specialist referral income, the number and revenue from referrals received differed by gender, as did the odds of receiving a referral from a physician of the same gender. Future research should examine the effectiveness of different policies to address this gap, such as a centralized, gender-blinded referral system.

Policies for enhancing patent quality: Evidence from renewable energy technology in China

Understanding the policy effectiveness in renewable energy technology innovation is essential to advancing the global energy transition. This paper constructs a patent quality index for renewable energy technology, which contributes to a better expression of technology innovation by incorporating time change factors. In order to study the effectiveness of government policies for enhancing technology patent quality in renewable energy, we first categorize policies according to the regulation intensity and technological characteristics, and use a sys-GMM model to assess the policy effectiveness. We further focus on heterogeneous effects of different policies on diverse technologies and region. The analysis shows that the economic incentive policies and guiding demonstrative policies better improve the quality of patent, but the effects vary across different energy technology types and regions. This paper suggests local government to prudently use the high regulatory strategies, and the design of policy mix should consider the effect of policies, technical and regional heterogeneity.

Social and Institutional Factors Affecting Language Learning Activities

This article aims to contribute to an understanding of how social and institutional factors affect the language learning environments of university students studying less-commonly taught languages (Turner, 1958), at beginner level by distance online. The empirical material is drawn from longitudinal case studies of students who enrolled in beginner-level distance courses in LCTLs at a regional Swedish university in the early 2010s. The study supports previous research illustrating the importance of sociocultural factors in learning activities. Furthermore, the study adds to research showing that for LCTLs an online learning context provides affordances that simply may not exist in campus settings and makes the study of LCTLs accessible to people for whom it would otherwise not be, an important contribution to linguistic diversity. The novel finding of this study is the direct and clearly articulated effect of different policies and frameworks on individuals’ choices of how, when and where to study, which suggests a need to examine further the ways that government and supranational entities shape the decisions made by adult learners.

The COVID-19 pandemic in various restriction policy scenarios based on the dynamic social contact rate

The social contact rate has influenced the transmission of COVID-19, with more social contact resulting in more contagion cases. We chose 18 countries with the most confirmed cases in the first 200 days after the Wuhan lockdown. This was the first study using the dynamic social contact rate to simulate the epidemic under diverse restriction policies over 500 days since the COVID-19 outbreak. The developed General Dynamic Model suggested that the probability of contagion ranged from 12.52% to 39.39% in the epidemic. The geometric mean of the social contact rates differed from 18.21% to 96.00% between countries. The restriction policies in developed economies were 3.5 times more efficient than in developing economies. We compare the effectiveness of different policies for disease prevention and discuss the influence of policy adjustment frequency for each country. Maintaining the tightest restriction or alternate tightening and loosening restrictions was recommended, with each having an average 72.45% and 79.78% reduction in maximum active cases, respectively.

What are the Sustainable Water Policies in Central Regions of Iran? An Integrated Water Resource Management Model

The efficient and optimal management of water resources is of great importance due to the strong dependence of human life on water. Also, the availability and utilization of the existing water resources in regions with water shortages, such as the Middle East, impose high social, economic, and environmental costs. Therefore, water resource management policies should incorporate all aspects of supply, allocation, control, and monitoring of resources. This study provides a dynamic simulation model for water resource management in the Yazd province of Iran to examine the effect of different policies on the other variables and choose the most suitable policies over time. For this purpose, we used a system dynamics approach to propose an integrated water resources management model, considering a comprehensive view of different aspects. The proposed model included over 230 influential variables in water resources, along with economic, demographic, technological, agricultural, industrial, public policy-making, water demand-supply, and virtual water volume subsystems. After we validate the model, we define three scenarios (optimistic, baseline, and pessimistic) and four policy packages (i.e., business as usual, focusing on economic development, focusing on sustainable development based on ecological balance, and focusing on water conservation) in the time horizons of 10, 20, and 50 years. The simulation results indicated that we require a reform in the Yazd economic development strategies through policies such as changing the cropping pattern and reducing water-consuming industries. Moreover, water supply enhancement by raising the inter-basin transfer of water alone cannot be an effective solution for reducing the water shortage in Yazd province and may even worsen the water shortage in the long run. We conclude that the “Focus on Water Conservation” policy is the best solution to reduce water shortage. The results of the baseline scenario show that adopting the FWC policy changes the increasing trend of water shortage in the province and decreases them from more than 53 billion m3 in the “Business as Usual” policy to less than 20 billion m3 in the year 2040. As the FWC would decrease more than 10 billion m3 of the virtual water level imbalance compared to BAU policy and make more investments in water efficiency plans, it preserves the current resources of water in the long run.

How spatial policies can leverage energy transitions − Finding Pareto-optimal solutions for wind turbine locations with evolutionary multi-objective optimization

Energy production from the power of wind is being increasingly promoted around the world to reach climate and energy targets and gain independence from fossil fuels. The identification of possible sites for on-shore wind energy production faces multiple challenges. National wind energy strategies cite various physical, ecological, social and economic constraints in defining wind turbine locations. Furthermore, the acceptance of new infrastructure is highly dependent on regional and local conditions.Consequently, the design of policy guidelines that simultaneously consider various constraints and goals in a spatially explicit manner is highly challenging. To tackle this challenge, we demonstrate how a state-of-the-art evolutionary optimization algorithm can inform policy-makers in leveraging various planning policies to optimize wind energy production. We investigate the spatial and non-spatial effects of different policies considering multiple planning targets and constraints. Moreover, we analyze trade-offs between different wind turbine planning targets. Based on the results, we outline several policy implications to support the identification of development areas for wind turbines in Switzerland. The proposed optimization method enables us to better understand the national planning horizon within a regional context and vice versa.

Quantifying the Effects of Different Containment Policies on Urban NO2 Decline: Evidence from Remote Sensing and Ground-Station Data

Cities exposed their vulnerabilities during the COVID-19 pandemic. Unprecedented policies restricted human activities but left a unique opportunity to quantify anthropogenic effects on urban air pollution. This study aimed to explore the underlying urban development issues behind these restrictions and support a sustainable transition. The data from ground stations and Sentinel-5P satellite were used to assess the temporal and spatial anomalies of NO2. Beijing China was selected for a case study because this mega city maintained a “dynamic zero-COVID” policy with adjusted restrictions, which allowed for better tracking of the effects. The time-series decomposition and prediction regression model were employed to estimate the normal NO2 levels in 2020. The deviation between the observations and predictions was identified and attributed to the policy interventions, and spatial stratified heterogeneity statistics were used to quantify the effects of different policies. Workplace closures (54.8%), restricted public transport usage (52.3%), and school closures (46.4%) were the top three restrictions that had the most significant impacts on NO2 anomalies. These restrictions were directly linked to mismatched employment and housing, educational inequality, and long-term road congestion issues. Promoting the transformation of urban spatial structures can effectively alleviate air pollution.

Energy Taxation Reform with an Environmental Focus in Portugal

Climate change has made the decarbonization of the different domestic economies a widely accepted and urgent priority. Yet, this is a very challenging task in a largely uncharted territory. In this context, in this paper, we address the issue of energy taxation reform with an environmental focus in Portugal. We do so using a multi-sector and multi-household dynamic computable general equilibrium model of the Portuguese economy. We analyze the environmental, macroeconomic, and distributional effects of different policies replacing current energy taxation with carbon taxation and, then, extend the carbon taxation to the levels necessary to achieve the IPCC 2018 emissions reduction targets. Our analysis indicates a clear path in the quest for decarbonization. First, replace energy taxes with a carbon tax; second, adopt the levels of carbon taxation necessary to achieve the emissions goals; third, use extra tax revenues from the carbon tax to reverse any potential adverse macroeconomic and distributional effects of carbon taxation. In the process, this would be a way around the pervasive problem of perverse fossil fuel subsidies, which would effectively disappear and, as such, would improve the efficiency of the tax system.

The Impact of “Dual-Control” Regulations on the Green Total Factor Efficiency of Shaoxing’s Industrial Sector

Promoting the decoupling between economic growth and carbon emissions through government intervention is very crucial for China to achieve carbon neutrality. This paper evaluates the green development performance with the help of the green total factor efficiency index and explores the impact of “Dual-Control” regulations on the green development of the industrial sector in Shaoxing using the differences-in-differences model. The results show that there are great diversities in the green development of different industries and that the energy-intensive industries have relatively poorer performance. The “Dual-Control” regulations significantly narrow the differences and promote the green development of Shaoxing’s industry but result in profit erosion for industrial enterprises owing to direct energy-saving expenditure and other indirect policy execution costs. The results of the dynamic analysis reveal that the negative economic impact has hysteresis and persistence. Different from previous studies, this paper considers the cross effects of different policies and examines the comprehensive effect of the policy package under the “Dual-Control” regulations. The conclusion provides a supplement to revealing the relationship between government regulation and energy conservation and emission reduction.

Accept or reject a ride? This is the problem

PurposeOnline ride-hailing platforms match drivers with passengers by receiving ride requests from passengers and forwarding them to the nearest driver. In this context, the low acceptance rate of offers by drivers leads to friction in the process of driver and passenger matching. What policies by the platform may increase the acceptance rate and by how much? What factors influence drivers' decisions to accept or reject offers and how much? Are drivers more likely to turn down a ride offer because they know that by rejecting it, they can quickly receive another offer, or do they reject offers due to the availability of outside options? This paper aims to answer such questions using a novel dataset from Tapsi, a ride-hailing platform located in Iran.Design/methodology/approachThe authors specify a structural discrete dynamic programming model to evaluate how drivers decide whether to accept or reject a ride offer. Using this model, the authors quantitatively measure the effect of different policies that increase the acceptance rate. In this model, drivers compare the value of each ride offer with the value of outside options and the value of waiting for better offers before making a decision. The authors use the simulated method of moments (SMM) method to match the dynamic model with the data from Tapsi and estimate the model's parameters.FindingsThe authors find that the low driver acceptance rate is mainly due to the availability of a variety of outside options. Therefore, even hiding information from or imposing fines on drivers who reject ride offers cannot motivate drivers to accept more offers and does not affect drivers' welfare by a large amount. The results show that by hiding the information, the average acceptance rate increases by about 1.81 percentage point; while, it is 4.5 percentage points if there were no outside options. Moreover, results show that the imposition of a 10-min delay penalty increases acceptance rate by only 0.07 percentage points.Originality/valueTo answer the questions of the paper, the authors use a novel and new dataset from a ride-hailing company, Tapsi, located in a Middle East country, Iran and specify a structural discrete dynamic programming model to evaluate how drivers decide whether to accept or reject a ride offer. Using this model, the authors quantitatively measure the effect of different policies that could potentially increase the acceptance rate.

Realizing Gender Diversity on Corporate Boards

This study examines the effectiveness of soft law through scrutinizing national policies for enhancing the proportion of women on corporate boards. Soft laws, which have less precision and obligation dimensions than conventional laws, are widespread within financial regulation. Despite this widespread use there relative performance is unexplored. To resolve this, we undertake a comparative examination of 14,012 firms from 99 nations, using a three-stage analysis to examine the effect of different policies, their format and influence of institutional factors on female board representation. We report that soft laws are effective for promoting gender equality on corporate boards. The effectiveness of policies is strongly influenced by the enforcement, implementation and compliance dimensions of policy, and institutional factors. Policies are most potent when enforced using a moderate level of sanctions, with a longer compliance period and a diversity target less distant from a firm’s precedent gender diversity level.

Evaluating the Importance of Monetary Policy Uncertainty: The Long- and Short-Term Effects and Responses.

Monetary policy changes have an irreplaceable impact on economic activity. Considering the close linkage among economic policies, we employ a bi-directional Granger causality test to investigate the potential linkages between monetary policy uncertainty (MPU) and other categorical economic policy uncertainty (CEPU) in the time and frequency domains. We consider all news-based U.S. categorical economic policy uncertainty indices (CEPU). All monthly CEPU indicators, covering January 1986 to January 2022, can be obtained from the website of Economic Policy Uncertainty. On an average, causality running from each CEPU to MPU is not apparent, while MPU can significantly affect six policy-related uncertainties: taxes, government spending, health care, national security, entitlement programs and regulation. A further frequency-domain study showed the dynamic changes in the relationship between them. For instance, we capture mid- and long-run causality running from tax uncertainty to MPU, while MPU has an impact on taxes in the medium run. Our findings provide policymakers with a better understanding of the nexus between MPU and other CEPU for formulating appropriate economic policies. Particularly, if a sectional government considers the long- and short-term effects of different policies when formulating strategies, risk transmission may be curbed to some extent.

Echo chambers and opinion dynamics explain the occurrence of vaccination hesitancy.

Vaccination hesitancy is a major obstacle to achieving and maintaining herd immunity. Therefore, public health authorities need to understand the dynamics of an anti-vaccine opinion in the population. We introduce a spatially structured mathematical model of opinion dynamics with reinforcement. The model allows as an emergent property for the occurrence of echo chambers, i.e. opinion bubbles in which information that is incompatible with one’s entrenched worldview, is probably disregarded. We scale the model both to a deterministic limit and to a weak-effects limit, and obtain bifurcations, phase transitions and the invariant measure. Fitting the model to measles and meningococci vaccination coverage across Germany, reveals that the emergence of echo chambers dynamics explains the occurrence and persistence of the anti-vaccination opinion in allowing anti-vaxxers to isolate and to ignore pro-vaccination facts. We predict and compare the effectiveness of different policies aimed at influencing opinion dynamics in order to increase vaccination uptake. According to our model, measures aiming at reducing the salience of partisan anti-vaccine information sources would have the largest effect on enhancing vaccination uptake. By contrast, measures aiming at reducing the reinforcement of vaccination deniers are predicted to have the smallest impact.

Evaluating Effects of Dynamic Interventions to Control COVID-19 Pandemic: A Case Study of Guangdong, China.

The emergence of different virus variants, the rapidly changing epidemic, and demands for economic recovery all require continual adjustment and optimization of COVID-19 intervention policies. For the purpose, it is both important and necessary to evaluate the effectiveness of different policies already in-place, which is the basis for optimization. Although some scholars have used epidemiological models, such as susceptible-exposed-infected-removed (SEIR), to perform evaluation, they might be inaccurate because those models often ignore the time-varying nature of transmission rate. This study proposes a new scheme to evaluate the efficiency of dynamic COVID-19 interventions using a new model named as iLSEIR-DRAM. First, we improved the traditional LSEIR model by adopting a five-parameter logistic function to depict the key parameter of transmission rate. Then, we estimated the parameters by using an adaptive Markov Chain Monte Carlo (MCMC) algorithm, which combines delayed rejection and adaptive metropolis samplers (DRAM). Finally, we developed a new quantitative indicator to evaluate the efficiency of COVID-19 interventions, which is based on parameters in and considers both the decreasing degree of the transmission rate and the emerging time of the epidemic inflection point. This scheme was applied to seven cities in Guangdong Province. We found that the iLSEIR-DRAM model can retrace the COVID-19 transmission quite well, with the simulation accuracy being over 95% in all cities. The proposed indicator succeeds in evaluating the historical intervention efficiency and makes the efficiency comparable among different cities. The comparison results showed that the intervention policies implemented in Guangzhou is the most efficient, which is consistent with public awareness. The proposed scheme for efficiency evaluation in this study is easy to implement and may promote precise prevention and control of the COVID-19 epidemic.

DUALIST POPULAR CONSTITUTIONALISM A Reply to Michelman

DUALIST POPULAR CONSTITUTIONALISM A Reply to Michelman

Analyzing the Effects of Different Policies and Strictness Levels on Monthly Corona Virus Case Increase Rates using Machine Learning and Big Data Analysis

The corona virus is one of the most unprecedented events of recent decades. Countries struggled to identify appropriate COVID-19 policies to prevent virus spread effectively. Although much research has been done, little focused on policy effectiveness and their enforcement levels. As corona virus cases and death numbers fluctuated among countries, questions of which policies are most effective in preventing corona virus spread and how strict they should be implemented have yet to be answered. Countries are prone to making policy and implementation errors that could cost lives. This research identified the most effective policies and their most effective enforcement levels through data analysis of 12 common coronavirus policies. A monthly case increase rate prediction model was developed to enable decision makers to evaluate the effectiveness of COVID-19 policies and their enforcement levels so that they can implement policies efficiently to save lives, time, and money.