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Probably because of their interpretability and transparent nature, synthetic controls have become widely applied in empirical research in economics and the social sciences. This article aims to provide practical guidance to researchers employing synthetic control methods. The article starts with an overview and an introduction to synthetic control estimation. The main sections discuss the advantages of the synthetic control framework as a research design, and describe the settings where synthetic controls provide reliable estimates and those where they may fail. The article closes with a discussion of recent extensions, related methods, and avenues for future research.

Infectious disease modelling has played an integral part of the scientific evidence used to guide the response to the COVID-19 pandemic. In the UK, modelling evidence used for policy is reported to the Scientific Advisory Group for Emergencies (SAGE) modelling subgroup, SPI-M-O (Scientific Pandemic Influenza Group on Modelling-Operational). This Special Issue contains 20 articles detailing evidence that underpinned advice to the UK government during the SARS-CoV-2 pandemic in the UK between January 2020 and July 2020. Here, we introduce the UK scientific advisory system and how it operates in practice, and discuss how infectious disease modelling can be useful in policy making. We examine the drawbacks of current publishing practices and academic credit and highlight the importance of transparency and reproducibility during an epidemic emergency. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.

Background National energy and climate scenarios are typically simulated or optimised using sectoral or energy system models, which include a large number of model settings and scenario assumptions. However, their realisation is contingent upon framework conditions and policy settings, which are often included in accompanying narrative scenarios. This paper therefore proposes refocussing the model-policy logic towards directly modelling policy effects. Applying this approach to the case of German passenger transport, I focus on demand-side policies and use open-source databases and models to develop a module for the translation of policies into model parameters. Results Separate model runs were used to test a ceteris paribus policy reference scenario for 2035, the marginal impacts of modelled single policy effects, and a joint policy package scenario. Relative to the reference, demand-side policies show significant impacts: an annual reduction of 355 bn person-kilometres (30%) and a reduction of car-owning households from 95 to 90% in rural areas and from 76 to 64% in urban areas. The resulting mode shift decreases car-driven kilometres by 400 bn and increases public transport by 45 bn per year. This may reduce GHG emissions by an additional 30 Mt (or 33%) relative to the reference in 2035. Conclusions Transport demand policies can significantly mitigate GHG, calling for a stronger policy focus beyond the much-studied shift to electric vehicles. While further research and model development are needed, the feasibility of policy scenario modelling increases its utility for policy-making.

Background Government alcohol monopolies were created in North America and Scandinavia to limit health and social problems. The Swedish monopoly, Systembolaget, reports to a health ministry and controls the sale of all alcoholic beverages with > 3.5% alcohol/volume for off-premise consumption, within a public health mandate. Elsewhere, alcohol monopolies are being dismantled with evidence of increased consumption and harms. We describe innovative modelling techniques to estimate health outcomes in scenarios involving Systembolaget being replaced by 1) privately owned liquor stores, or 2) alcohol sales in grocery stores. The methods employed can be applied in other jurisdictions and for other policy changes. Methods Impacts of the privatisation scenarios on pricing, outlet density, trading hours, advertising and marketing were estimated based on Swedish expert opinion and published evidence. Systematic reviews were conducted to estimate impacts on alcohol consumption in each scenario. Two methods were applied to estimate harm impacts: (i) alcohol attributable morbidity and mortality were estimated utilising the International Model of Alcohol Harms and Policies (InterMAHP); (ii) ARIMA methods to estimate the relationship between per capita alcohol consumption and specific types of alcohol-related mortality and crime. Results Replacing government stores with private liquor stores (Scenario 1) led to a 20.0% (95% CI, 15.3–24.7) increase in per capita consumption. Replacement with grocery stores (Scenario 2) led to a 31.2% (25.1–37.3%) increase. With InterMAHP there were 763 or + 47% (35–59%) and 1234 or + 76% (60–92%) more deaths per year, for Scenarios 1 and 2 respectively. With ARIMA, there were 850 (334–1444) more deaths per year in Scenario 1 and 1418 more in Scenario 2 (543–2505). InterMAHP also estimated 10,859 or + 29% (22–34%) and 16,118 or + 42% (35–49%) additional hospital stays per year respectively. Conclusions There would be substantial adverse consequences for public health and safety were Systembolaget to be privatised. We demonstrate a new combined approach for estimating the impact of alcohol policies on consumption and, using two alternative methods, alcohol-attributable harm. This approach could be readily adapted to other policies and settings. We note the limitation that some significant sources of uncertainty in the estimates of harm impacts were not modelled.

In recent years, carbon dioxide (CO2) emissions have increased at the fastest rates ever recorded. This is a trend that contradicts global efforts to stabilise greenhouse gas (GHG) concentrations and prevent long-term climate change. Over 90% of global transport relies on oil-based fuels. The continued use of diesel and petrol raises concerns related to oil costs, supply security, GHG emissions, and the release of air pollutants and volatile organic compounds. This study explored electric vehicle (EV) charging networks by assessing environmental impacts through GHG and petroleum savings, developing dynamic pricing strategies, and forecasting infrastructure needs. A substantial dataset of over 259,000 EV charging records from Palo Alto, California, was statistically analysed. Machine learning models were applied to generate insights that support sustainable and economically viable electric transport planning for policymakers, urban planners, and other stakeholders. Findings indicate that GHG and gasoline savings are directly proportional to energy consumed, with conversion rates of 0.42 kg CO2 and 0.125 gallons per kilowatt-hour (kWh), respectively. Additionally, dynamic pricing strategies such as a 20% discount on underutilised days and a 15% surcharge during peak hours are proposed to optimise charging behaviour and improve station efficiency.

With China’s profound demographic transition, population health issues have emerged as critical determinants of sustainable national development. Since 2001, the Chinese government has implemented numerous population health policies; however, the absence of a systematic, standardized, and quantifiable evaluation framework has constrained the scientific rigor and effectiveness of policy assessment. This study aims to develop a Policy Modeling Consistency (PMC) Index model tailored to China’s context, enabling systematic quantitative evaluation of population health policies to identify strengths and limitations, thereby establishing an evidence base for policy optimization.This study employs text mining combined with the PMC Index model to construct an evaluation framework comprising 9 primary variables and 40 secondary variables. Twelve representative population health policies were quantitatively assessed, with results visualized through PMC surface maps to analyze internal policy consistency and multidimensional performance.The evaluation revealed that 8 policies achieved “excellent” ratings (PMC index: 6.00-7.31), while 4 received “good” ratings (PMC index: 5.59–5.78), indicating generally high quality with notable variations across China’s population health policies. Most policies demonstrated consistent strengths in timeliness, evaluation mechanisms, and transparency. However, significant disparities emerged in policy functions, target populations, and policy objectives.Through development of the PMC Index model, this study provides systematic, multidimensional quantitative evaluation of China’s population health policies, introducing novel insights and methodological approaches for policy optimization. The findings offer substantial theoretical and practical contributions toward enhancing the scientific precision and effectiveness of China’s population health policies, ultimately promoting improved population health outcomes.

Forest-based measures to mitigate climate change are increasingly being acknowledged as options for meeting the global targets of the Paris Agreement. In this context, carbon pricing systems may foster carbon sequestration in forests and harvested wood products. Forest sector models (FSMs) are established simulation instruments for assessing the possible impacts of carbon pricing systems on forest-based mitigation potentials, forestry, and forest product markets. However, the characteristics of the implemented carbon pricing systems differ among these assessment tools. To map and evaluate this variability, we conducted a scoping review of carbon pricing systems in FSMs, following the RepOrting standards for Systematic Evidence Syntheses (ROSES). Drawing on 49 modeling studies, including 351 scenarios, we provide an overview of the state-of-the-art methods for implementing carbon pricing systems in FSMs, discuss technical aspects and uncertainties, and identify possible future research trends. Our results reveal similarities in the types of carbon pricing systems and differences regarding the system boundaries and carbon price-related characteristics of the implemented systems. Geographically, since most studies target either the Northern Hemisphere or the world, we found a lack of in-depth assessments in tropical and boreal countries. Further, additionality, permanence, and leakage of forest-related mitigation measures are addressed using different approaches with varying practicability. Mostly, the observed heterogeneity in the implemented carbon pricing systems can be related to the attributes of pre-existing modeling frameworks. We systematically collect and highlight tools to analyze the role of forest-based mitigation measures in the context of climate commitments and outline carbon pricing policies that could support their implementation. For future studies, the assessment of policy mixes involving carbon pricing and the inclusion of climate change effects on forest growth appear to be crucial for delivering more robust projections of forest-based mitigation potentials and, hence, for increasing the reliability of the forest-based contribution to climate mitigation actions.

This article contributes to the debate on fiscal multipliers, in the context of an estimated dynamic stochastic general equilibrium model, featuring a rich fiscal policy block and a transmission mechanism for government spending shocks. I find the multiplier for government spending to be 1.07, which is largest on impact. The multipliers for labor and capital tax on impact are 0.13 and 0.34, respectively. The effects of tax cuts take time to build and exceed stimulative effects of spending by 12–20 quarters. I carry out counterfactual exercises to show how alternative financing methods and expected monetary policy have consequences for the size of fiscal multipliers.

The carbon emission of China’s industry accounts for more than 70 % of the total in the nation, thus the implementation of carbon emission quota trading in industry is of great importance to realize China’s national carbon emission reduction targets. Meanwhile, the allocation of carbon emission quota among sectors or enterprises proves the first and critical step. For this reason, this paper constructs a comprehensive index combined with the subjective, objective and linear combination weighting methods to allocate carbon emission quotas among the 39 sectors of China’s industry in 2020 based on the level of 2015, and employs the input-oriented ZSG-DEA model to examine the efficiency of allocation solutions in 2020. The results indicate that, first, when carbon emission reduction capacity, responsibility and potential are considered for the comprehensive index of carbon emission quota allocation, the mitigation responsibility plays a relatively higher role than other two indicators. Second, all of the subjective, objective and linear combination weighting methods can be used for effective allocation of carbon emission quotas, and the former two methods have less advantage in light of efficiency. Third, six key industrial sectors are respectively allocated over 500 million tonnes of carbon emission quotas in 2020, which together account for 91.77 % of the total in the industry. Finally, the final carbon emission quota allocation solution reflects both the equity and efficiency principles and achieve the Pareto optimal state.

Data-intensive research, including policy modelling, poses some distinctive challenges for efforts to mainstream public involvement into health research. There is a need for learning about how to design and deliver involvement for these types of research which are highly technical, and where researchers are at a distance from the people whose lives data depicts. This article describes our experiences involving members of the public in the SIPHER Consortium, a data-intensive policy modelling programme with researchers and policymakers working together over five years to try to address health inequalities. We focus on evaluating people’s experiences as part of Community Panels for SIPHER. Key issues familiar from general public involvement efforts include practical details, careful facilitation of meetings, and payment for participants. We also describe some of the more particular learning around how to communicate technical research to non-academic audiences, in order to enable public scrutiny of research decisions. We conclude that public involvement in policy modelling can be meaningful and enjoyable, but that it needs to be carefully organised, and properly resourced. Plain English summary Actively involving members of the public is less common in ‘data-intensive health research’ (health research which does not create new data but focuses on analysing big existing datasets of statistics) than in conventional health research. ‘Computational policy modelling’ is an example of data-intensive health research where public involvement is not yet standard practice. This article describes our experiences involving members of the public in the SIPHER Consortium, a policy modelling programme with researchers and policymakers working together over five years to try to address health inequalities. This paper focuses on evaluating people’s experiences as part of Community Panels for SIPHER. We brought together people with lived experience of health inequalities into three Community Panels, and we met for half a day 3-4 times a year to discuss and give feedback on the research. At first, it was difficult for Panel members to understand the research. Researchers had to try harder to avoid jargon, explain their work in plain English, and focus on the impact of the research in the ‘real world’. Both the researchers and the Panel members learned how to communicate better over repeated meetings. Over time, we managed to have meaningful discussion of the choices researchers were making, so Panels could see their impact on the research. It was important that details of the meetings – planning meetings carefully so everyone feels welcome and valued, providing support with digital technology, financially rewarding people for their time – were taken seriously. We conclude that public involvement in policy modelling can be meaningful and enjoyable, but that it needs to be carefully organised, and takes time and money to get right.