Evaluation Of Policy Measures Research Articles

Recent developments in urban freight analytics for collaborative city logistics

This paper presents recent developments in urban freight analytics for collaborative city logistics. City logistics provides innovative solutions using emerging technologies such as Information and Communication Technologies (ICT), Intelligent Transport Systems (ITS), Internet of Things (IoT), big data, Artificial Intelligence (AI), autonomous vehicles and robots. Sharing data between public and private entities is important for modelling, planning and evaluating policy measures related to city logistics. Mathematical models such as multi-agent models can replicate the behaviour of stakeholders in city logistics to help make a better decision considering logistics costs, the environment and energy consumption. Autonomous robots and drones can be used for urban deliveries to minimise the risk of infections during the pandemic. Hyperconnected city logistics combines the concepts of the Physical Internet and city logistics to create more sustainable urban distribution systems by promoting more collaborative, integrated and open networks. Digital twins aim to provide a virtual system for replicating physical systems and their processes that allow the dynamic status of the environment and physical elements in the real world to be updated using various sources of information. Warehouse sharing systems are logistics platforms where the available capacity of warehouses can be matched with the shippers/factories looking to temporarily store their goods before further distribution.

Improving the methodology of national energy balances to adapt to the energy transition

An adequate information basis is important for designing and evaluating policy measures to reach international climate goals. Current energy statistics are primarily based on the UN's International Recommendations for Energy Statistics (IRES). We have examined changes in the energy system and how they can be depicted in energy balances, using IRES methodology as the benchmark. We have found that the increasing variety in energy generation through prosumers cannot be illustrated currently. In the manufacturing industries, state-of-the-art balancing limits the depiction of autoproducers' output other than electricity and heat. Their numbers will increase due to hydrogen demand for decarbonisation, among others. In efforts to inform necessary decision-making regarding decarbonisation, an additional focus must also be set on the representation of energy services in demand. Including useful energy categories allows the development of specific useful energy demands, enabling application-driven technology and energy carrier deployment. To remedy the identified shortcomings, the Sectoral Gross Energy Balance Border enables the identification of involved economic units (e.g. manufacturing industry sectors, households, services and energy industries). It features a sector-interrelated approach, in which energy flows follow the physical location of energy operation. Thereby, energy balances can illustrate the multiple transformations in the energy system and better inform policymaking.

A CRITICAL REVIEW OF YOUTH-ORIENTED POLICIES IN THE REPUBLIC OF MOLDOVA FROM THE PERSPECTIVE OF YOUTH TRANSITION REGIME

Youth issues have long been a focus of policy in Moldova, despite an uneven trend over time. The aim of the article is to analyze the main policy documents devoted to youth within key policy areas that invest in the development and efficient use of human capital from the perspective of the Youth Transition Regime. A mixed-method approach was applied: situation and contextual analysis, review and analysis of policy documents, reports and studies targeting young people. The review and analysis of policy framework has revealed that although youth issues are promoted through various policies, there is a lack of an integrated approach and a comprehensive evaluation mechanism on the implementation of youth policies, including cross-sectoral dimension. Youth-related policies falls into a transitional regime characterized by an integrated education system, high rate of early leavers from education and training, high level of youth rate Not in Education Employment or Training – NEET, low popularity/prominence of Vocational Education and Training, poor concordance between the education system and the labour market. The evaluation of policy measures intended to facilitate the transition of young people to adulthood shows that individualized and compensatory approaches predominate. Policy actions are focused mainly on youth skills development and less on structural policy measures in crosssectoral areas. There is a need to review and adapt youth policies and strategies to the current and real needs of youth by promoting participatory approaches that would reflect the diversity of youth in Moldova, especially in socially vulnerable groups.

COVID-19 ensemble models using representative clustering

In response to the COVID-19 pandemic, there have been various attempts to develop realistic models to both predict the spread of the disease and evaluate policy measures aimed at mitigation. Different models that operate under different parameters and assumptions produce radically different predictions, creating confusion among policy-makers and the general population and limiting the usefulness of the models. This newsletter article proposes a novel ensemble modeling approach that uses representative clustering to identify where existing model predictions of COVID-19 spread agree and unify these predictions into a smaller set of predictions. The proposed ensemble prediction approach is composed of the following stages: (1) the selection of the ensemble components, (2) the imputation of missing predictions for each component, and (3) representative clustering in application to time-series data to determine the degree of agreement between simulation predictions. The results of the proposed approach will produce a set of ensemble model predictions that identify where simulation results converge so that policy-makers and the general public are informed with more comprehensive predictions and the uncertainty among them.

Mitigating energy demand sector emissions: The integrated modelling perspective

Limiting climate change below a given temperature will require fundamental changes in the current energy system, both in the energy supply and the energy demand sectors. Previous global model-based analyses, however, have focused mostly on energy supply transformations. Therefore, in this study we respond to this knowledge gap by analysing the future energy demand projections in both baseline and climate policy scenarios of global models in detail. We examine the projections for the industry, transport and buildings sectors across four models and three different reference scenarios from the Shared-Socioeconomic Pathway framework by applying a decomposition analysis. We compare the projected demand side mitigation efforts to a more detailed, sector-specific, technology-oriented assessment of demand-side abatement potential for the year 2030. Without climate policy, model-based projections show that baseline emissions can grow rapidly in industry and transport sectors, but are also highly uncertain across models. The decomposition analysis shows that the key uncertainty across the global scenarios is the projected final energy per capita. For modellers therefore there lies a challenge to better understand drivers of future energy efficiency and service demand, that contribute to the projected energy demand. This model enhancement would moreover allow to evaluate policy measures that can impact this relation. The technology assessment estimates that in particular in the transport and buildings sector there is a higher potential to reduce demand-side emissions through energy efficiency improvements than implemented in the scenarios. Improved insulation, higher electric vehicle penetration rates and modal shift for example could reduce final energy demand to lower levels in the short term than currently projected, reducing the dependency on fuel switching required in current scenarios to meet the stringent climate targets.

Cloud computing and its impact on the Japanese macroeconomy–its oligopolistic market characteristics and social welfare

The cloud computing market is rapidly expanding and changing the nature of ICT across all sectors; cloud computing transforms ICT from a tool dependent upon investment and physical ownership to one that can easily make use of outside resources. On the other hand, cloud computing services are being provided not only for simple data storage, but for many purposes through several different service models, such as SaaS (Software as a Service), PaaS (Platform as a Service) and IaaS (Infrastructure as a Service). Due to, among other factors, the nature of its network externalities, the market seems to be gradually shifting to a market with oligopolistic characteristics in which the services are provided by a limited number of big-name firms. In this paper, the impact of the shift of the cloud computing market to a market with oligopolistic characteristics on utility-based social welfare is quantitatively estimated and analyzed utilizing DSGE (Dynamic Stochastic General Equilibrium) model-based simulation. The main objectives of the paper, rather than to ascertain the exact change of utility-based social welfare, are (1) to determine whether there is a (realistic) shifting path of macroeconomic variables in Japan’s case, under the shift and (2) to illustrate the dynamic interaction of the macroeconomic variables. Utility-based social welfare is chosen, because for the purpose of evaluating policy measures it would be more suitable than GDP. Also, several kinds of policy measures for the sound development of the cloud computing market that the industry and government can take are discussed in a sense related to a market with oligopolistic characteristics.

Landscape-scale simulations as a tool in multi-criteria decision making to support agri-environment schemes

Increasing concerns over the environmental impacts of agriculture in Europe has led to the introduction of agri-environment schemes (AES) into the Common Agricultural Policy to help mitigate biodiversity loss by encouraging farmers with subsidies for implementing environmentally-friendly farming techniques. However, effectiveness of AES has been mixed and only partially successful in achieving desired outcomes. To improve effectiveness and reduce costs, multi-criteria decision analysis (MCDA) can help support decision-making and determine the most effective management action. Although MCDA has great potential for evaluating policy measures, it rarely considers the context-dependency of species responses to management practices across different landscapes. Landscape simulations can, therefore, be valuable for reducing the uncertainties when predicting the consequences of management actions. A potential suitable simulation system is the Animal, Landscape, and Man Simulation System (ALMaSS), a mechanistic simulation with can improve MCDA with the automatic integration of landscape context and a species ecology and behaviour. The aim of this study was to demonstrate the effectiveness of ALMaSS in evaluating AES management practices across different landscapes and estimate their ability to achieve the proposed conservation outcomes in three species of conservation interest. In this study, the effect of a particular management strategy on a species was dependent on the landscape context, in our case, a combination of landscape structure and the type and distribution of farms, and varied depending on the metrics being measured. We demonstrate how simulations can be used for MCDA to select between management strategies with different costs. Despite the complexity of ALMaSS models, the simulation results provided are easy to interpret. Landscape simulations, such as ALMaSS, can be an important tool in multi-criteria decision making by simulating a wide range of managements and contexts and provide supporting information for filtering management options based on specific conservation goals.

Characteristics of Mycobacterium bovis infected herds tested with the interferon-gamma assay

The IFN-γ (interferon gamma) assay is used in Ireland as an ancillary diagnostic test to the single intradermal comparative tuberculin test (SICTT) to maximise the detection of Mycobacterium bovis infected animals (bTB) in cattle herds. Understanding the relationships between herd and animal risk factors and IFN-γ test results is critical to enable the development and evaluation of policy measures on how best to use the test. In this study, we set out to characterise Irish herds with IFN-γ test positive animals in terms of herd size, number of SICTT reactors and number of IFN-γ positive tests, and to evaluate the IFN-γ test in terms of the test cut-off values. The results showed that larger herds with more SICTT reactors were likely to have more IFN-γ positives in the herd, and herds with an IFN-γ test positive animal that was also positive for bTB lesions at post-mortem had higher numbers of IFN-γ positive animals in the herd. Raising the cut-off values for the IFN-γ test only marginally decreased the combined sensitivity of the IFN-γ and the SICTT for diagnosis of bTB lesioned animals. The analysis has provided valuable information on the performance of the IFN-γ test as it is used under current bTB infection levels in Ireland.

What works? An ANFIS-based policy evaluation framework for electric vehicle technology development

The application of socio-technical transitions analysis into realms such as sustainable mobility requires understanding the efficacy of policy measures ex ante. Electrification of the vehicle drivetrain is one possible solution to achieve carbon emission targets. National governments are developing policy measures to encourage electric vehicle (EV) technologies. To evaluate policy measures, a framework based on adaptive neuro-fuzzy inference system (ANFIS) was developed. For data generation, EV innovation policies of EU, USA, Japan, Germany, France and UK were analysed and compared with the patent filings in those regions. The training and validation of the framework shows that the model is able to predict the development of EV technologies. The model is subsequently applied to Austria to evaluate three proposed policy scenarios. This paper concludes that the developed framework might play a significant role for assisting EV innovation policy-making by enabling ex-ante assessment of the effects of different policy-mixes on the technical change.

What works? An ANFIS-based policy evaluation framework for electric vehicle technology development

The application of socio-technical transitions analysis into realms such as sustainable mobility requires understanding the efficacy of policy measures ex ante. Electrification of the vehicle drivetrain is one possible solution to achieve carbon emission targets. National governments are developing policy measures to encourage electric vehicle (EV) technologies. To evaluate policy measures, a framework based on "adaptive neuro-fuzzy inference system" (ANFIS) was developed. For data generation, EV innovation policies of EU, USA, Japan, Germany, France and UK were analysed and compared with the patent filings in those regions. The training and validation of the framework shows that the model is able to predict the development of EV technologies. The model is subsequently applied to Austria to evaluate three proposed policy scenarios. This paper concludes that the developed framework might play a significant role for assisting EV innovation policy-making by enabling ex-ante assessment of the effects of different policy-mixes on the technical change.

Evaluating Policy Measures to Tackle Undeclared Work: The Role of Stakeholder Collaboration in Building Trust and Improving Policy-Making

The aim of this paper is to examine and analyse the realm of policy evaluation approaches and methods as they relate to assessing measures to tackle undeclared work. The discussion is set at the backdrop of a brief review of the more prominent theoretical and conceptual considerations in the policy evaluation literature. The paper then investigates results from policy assessments and evaluations illuminated in the previous GREY working papers, as well as some selected from the Eurofound database. The analysis of a limited sample of available policy evaluations and results demonstrates that a common probable cause of policy failure with regards to measures to tackle undeclared work in the GREY countries is an apparent lack of knowledge and understanding of the needs, attitudes and abilities of policy beneficiaries and targets. This strongly suggests non-existent or inadequate engagement of all stakeholders in the initial policy evaluation or feasibility study stage of the policy-making process. This exploratory effort has identified the adoption of a collaborative stakeholder strategy to tackling the undeclared economy as a major recommendation for moving forward.

Biodiversity in cultural landscapes: influence of land use intensity on bird assemblages

Land use changes and intensification have been amongst the major causes of the on-going biodiversity decline in Europe. A better understanding and description of how different levels of land use intensity affect biodiversity can support the planning and evaluation of policy measures. Our study investigates how land use-related landscape characteristics affect bird diversity, considering different spatial scales and species groups with characteristic habitat use. We used breeding bird census data from 2693 observation points along 206 transects and applied a random effects hurdle model to describe the influence of the landscape characteristics altitude, forest proportion, patch density, land cover diversity, and land use intensity on avian species richness. Land use intensity and related landscape characteristics formed an important explanatory variable for bird richness. Increasing land use intensity was accompanied by a decrease in bird species richness. While forest bird richness decreased with a decreasing amount of forest cover, farmland species richness increased. This led to a bird diversity peak in extensively used semi-open landscapes. The influence of land cover diversity on species richness was small. Increasing patch density had positive effects on forest birds, but affected farm birds negatively. The strongest correlation between land use-based indicators and bird diversity was determined using spatial indicators at a close range around observation points (100–500 m radius). Our results assist interpretation of the Pan-European Common Bird Indices and emphasize the importance of using multifaceted and thoroughly selected indicators in the context of biodiversity monitoring and decision-making support.

Ex‐ante Evaluation of Policy Measures: Effects of Reward and Punishment for Fertiliser Reduction in Palm Oil Production

AbstractPalm oil production creates negative externalities, e.g. through intensive fertiliser application. Policies to limit externalities need an effective, sustainable and efficient measure We use a business simulation game in a framed field experiment in Indonesia to test ex‐ante different incentives for reducing such negative externalities. This setting allows inclusion of adequate contextual features, required for reasonable ex‐ante evaluation of policy measures. The different designs of the test incentives (either a reward or punishment) varied in their magnitude and probability of occurrence but with constant effects on expected income. Results show that participants react differently to these incentives, indicating that the design can contribute significantly to effectiveness, sustainability or efficiency. A high reward with a low probability was found to be the most effective and sustainable incentive. Moreover, for the most efficient design, a low and certain reward is indicated.

Financing in SMEs: Case of the Baltic States

Access to finance represents one of the most significant challenges for SMEs’ entrepreneurs. To ensure SMEs creation, existence and growth it is vitally important to understand the financing needs of SMEs and entrepreneurs, the main obstacles to finance availability and accessibility. The study indicates the difficulties in SMEs financing for the three Baltic States and provides the governments and other stakeholders with a tool to understand SMEs’ financing needs. The study results highlight importance of alternative resources of external financing for small developing countries such as the Baltic ones and the need to support the design and evaluation of policy measures and to monitor the implications of financial reforms on SMEs’ access to finance.

Public Sector Governance to Implement Freight Vehicle Transport Management

This paper is based on the results of the research carried out by the PIARC Technical Committee B4 (Freight Transport and Inter-Modality, Working Group 3 (Urban freight management) in the 2008 - 2011 research cycle. This paper focuses on the procedure of planning, implementing and evaluating policy measures for urban freight transport. The title of the report indicating “Governance” is challenging in terms of the collaborative work necessary to solve difficult problems encountered by stakeholders. This paper provides the outline of guidance for the governance of urban freight transport in the public sector.

Road Transport for Tourism: Evaluating Policy Measures from Consumer Profiles

A rise in the number of tourists at many destinations and their increased mobility in host countries or regions have led to a growth in tourism-associated externalities. Although there is a trend for governments to centre their transport policies on the supply side, promoting the development of different infrastructures and services or intervening in tourism markets, this paper presents an alternative way of thinking by analysing tourist demand profiles to detect those that tend to opt for private transport. The authors investigate different strategies in tourism policies designed to reduce the number of tourists using private motor vehicle transport and to promote public, group or charter transport at destinations. Taking the Balearic Islands as a case study, discrete choice models are employed to assess different ways of modelling the transport demand function for hire cars, other private cars, public buses and coaches, and thus to identify the corrective potential of the proposed policies.

Emerging Techniques for Enhancing the Practical Application of City Logistics Models

This paper presents a review of emerging techniques for enhancing the practical application of city logistics models. A number of models have been applied to practical problems for evaluating policy measures of city logistics. These models can be categorised into two types: optimisation models, and simulation models. Optimisation models incorporate dynamic and stochastic elements, since the real urban freight transport faces varying demands and travel times. Simulation models typically use multi-agent systems, as multiple stakeholders are involved in planning city logistics schemes. Evaluation methodology is directly related to decision making of policy measures. Models for supporting decision making have become more important for obtaining a social acceptance as well as governance among private and public entities.

How much Confidence can we have in EU-SILC? Complex Sample Designs and the Standard Error of the Europe 2020 Poverty Indicators

If estimates are based on samples, they should be accompanied by appropriate standard errors and confidence intervals. This is true for scientific research in general, and is even more important if estimates are used to inform and evaluate policy measures such as those aimed at attaining the Europe 2020 poverty reduction target. In this article I pay explicit attention to the calculation of standard errors and confidence intervals, with an application to the European Union Statistics on Income and Living Conditions (EU-SILC). The estimation of accurate standard errors requires among others good documentation and proper sample design variables in the dataset. However, this information is not always available. Therefore, I complement the existing documentation on the sample design of EU-SILC and test the effect on estimated standard errors of various simplifying assumptions with regard to the sample design. It is shown that accounting for clustering within households is of paramount importance. Although this results in many cases in a good approximation of the standard error, taking as much as possible account of the entire sample design generally leads to more accurate estimates, even if sample design variables are partially lacking. The effect is illustrated for the official Europe 2020 indicators of poverty and social exclusion and for all European countries included in the EU-SILC 2008 dataset. The findings are not only relevant for EU-SILC users, but also for users of other surveys on income and living conditions which lack accurate sample design variables.

A Comparison of the Impacts of Aspects of Prospect Theory on WTP/WTAEstimated in Preference and WTP/WTA Space

The importance of willingness to pay (WTP) and its counterpart willingness to accept (WTA), in the evaluation of policy measures has led to a constant stream of research examining survey methods and model specifications seeking to capture and explain the concept of marginal rates of substitution as much as possible. Stated choice experiments pivoted around a reference alternative allow the specification of discrete choice models to accommodate aspects of Prospect Theory, in the particular reference dependence. This permits an investigation of theories related to loss aversion and diminishing sensitivitywidely documented within the literature. This paper seeks to examine a number of theoretical developments. In particular, the paper seeks to empirically examine a number of aspects related to decision making processes that are posited to exist by Prospect Theory, namely reference dependence, loss aversion and diminishing sensitivities. Unlike previous research which has examined these issues in the past, we examine these assumptions on WTP/WTA rather than on the marginal utilities of decision makers. In doing this, the paper simultaneously compares and contrasts different econometric forms, in particular estimating models in preference space with WTP/WTAs calculated post estimation versus models estimated directly in WTP/WTA space where WTP/WTAvalues are directly during estimation. We find evidence for reference dependence and loss aversion in WTP/WTA for different time attributes, however we find less compelling evidence for the existence of diminishing WTP/WTAs.

Improvements and Future Challenges for the Research Infrastructure: Firm Level Data

This article discusses the use of enterprise- and establishment-level data from official statistics to document stylized facts, to motivate assumptions used in formal theoretical models, to test hypotheses derived from theoretical models, and to evaluate policy measures. It shows how these data can be accessed by researchers in Germany today and reports on recent developments that will offer new and improved datasets that combine data collected in separate surveys and by different agencies. The paper makes three recommendations for future developments in this area: (1) change the law to make the combination of data collected by different producers easier, (2) combine firm-level data across national borders and make these data available for researchers, and (3) find ways to enable researchers in Germany to work with confidential firm-level data via remote access 24 hours a day and 365 days per year.