Policy Decision Support Tools Research Articles

POLICY IMPORT TAX INCENTIVE UTILISATION DURING THE COVID-19 PANDEMIC

The objective of a policy will be achieved if the policy targets are well-defined. However, it often happens that the policy targets do not utilise the public policies given optimally. Therefore, it is essential to predict the targeted group that will utilise the policy provided so that the policy implementation can be more efficient. This study uses the Data Mining method as a policy decision support tool to analyse the best predictive model of the PPh 22 Import Tax Incentive utilisation during the COVID-19 Pandemic. The predictive model can be practical and valuable for Indonesia's tax authority and policy analysis knowledge. This study uses administrative data of 43.547 taxpayers who are already utilising and not utilising the tax incentive, combined with the data mining method. The results showed that with the Random Forest algorithm, the utilisation of the tax incentive could be predicted with an accuracy above 94%. Furthermore, The Total Sales Value, The Export Value, and The Bonded Zone Category are the variables that mostly predict the utilisation of the tax incentive. Therefore, tax authorities should be able to utilize data mining as a tool to be able to implement tax incentive policies more efficiently and accurately.

Pharmacovigilance in emerging economies: modeling interaction among barriers

PurposeThe main purpose of this paper is to identify and rank various barriers to pharmacovigilance (PV) in context of emerging economies and examine their interrelationships using the interpretive structural modeling (ISM) approach. The result is a model that offers insights about how to achieve rational and safe use of medicines and ensure patient safety as realized through robust national PV systems.Design/methodology/approachThe paper develops a model to analyze the interactions among PV barriers using the ISM approach. Based on input from clinical and medical product development experts, PV barriers in emerging economies were identified and reviewed. The hierarchical interrelationships among these PV barriers were analyzed in context of their driving/dependence powers.FindingsFindings of the study identify key PV barriers—lack of resources/infrastructure, weak legislation, unfair burden of disease, lack of PV capacity, training, and enforcement authority—that drive, or strongly influence, other barriers and thwart implementation of robust national PV systems in emerging economies. Pharmaceutical industry factors were PV barriers that were identified as autonomous, implying their relative disconnection from other barriers, and patient PV practices barrier was strongly dependent on other barriers.Research limitations/implicationsThe paper offers policy- and decision-makers alike with a framework to support further research into interdependencies among key PV barriers in emerging economies. It can serve as an impetus for further research with potential to broadening the understanding of how and why PV systems may be rendered ineffective. Future studies can be planned to apply the ISM approach to study PV barriers in the context of developed economies and draw lessons and implications for policy- and decision-makers by contrasting results from these studies.Practical implicationsThis paper contributes to the understanding of the multifaceted nature of PV and its barriers. The proposed approach gives public health decision-makers a better comprehension of driver PV barriers that have most influence on others versus dependent PV barriers, which are most influenced by others. Also, knowledge, attitude and practices of patients and caregivers can also be critical PV barriers in emerging economies. This information can be instrumental for public health policymakers, government entities, and health/PV practitioners to identify the PV barriers that they should prioritize for improvement and how to manage trade-offs between these barriers.Social implicationsPV barriers in emerging economies, as compared to developed economies, are inherently different and need to be examined in their specific context. The hierarchical ISM model suggests that resources and regulation initiatives by governments in emerging economies lead to through informed/enabled pharmaceutical supply chain players and eventually drive PV-specific knowledge, attitude, and practice outcomes improvements across their populace.Originality/valueThis paper highlights the deployment of ISM approach as a health policy decision support tool in the identifying and ranking barriers to effective PV systems in emerging economies, in terms of their contextual relationships, to achieve a better understanding as to how these interrelationships can affect national PV system outcomes.

Modelling national policy making to promote bioenergy in heat, transport and electricity to 2030 – Interactions, impacts and conflicts

Governments must increase bioenergy use to realise the Paris agreement ambition. Most countries have limited biomass resources and policy goals beyond carbon reduction. This can lead to policy incoherence. Previous studies tended to focus on one end-use sector or on optimising CO2 reduction. This study goes beyond optimisation approaches and investigates cross sector impacts of bioenergy policy proposals via simulation methods for policy proposals in Ireland. As an EU member with ambition for increased bioenergy use, Ireland is a useful case to examine trade-offs. Using the BioHEAT policy decision support tool (Durusut et al., 2018) we find policy in the heat and transport sector close the gap to Ireland's 2030 climate targets by 3%. Policy supporting co-firing of biomass with fossil-fuel to produce electricity increases emissions by 8.3 MtCO2 overall and reduces the policy impact on national climate targets by 63%. Co-firing uses more of the available biomass resources and this limits renewable uptake in the heat sector. Coal conversions and the use of advanced biofuels are found to rely on high availability of imports. Policy supporting biomass use in the power sector may make national climate targets less achievable for EU countries.

BioHEAT: A policy decision support tool in Ireland’s bioenergy and heat sectors

Bioenergy is likely to play a key role in decarbonising the energy system. The versatility of bioenergy as a transport, heat or electricity fuel is one of its key strengths but can add to the complexity of policy design. Polices aimed at stimulating bioenergy use in one end use sector should consider the impacts of use and uptake in the others.This paper details a methodology for an integrated bioenergy and heat policy decision support tool that is being used to inform policy in Ireland. The previous literature has focused on individual supply chain optimisation, plant sizing and plant locations from an operator’s perspective. The BioHEAT model is a techno-economic model that accounts for the co-dependencies between the end use sectors. It extends the approach to supply chain specification in the literature to incorporate a novel representation of consumer decision making in the heat sector and the flexibility to model various policy types in heat, electricity and transport sectors from a policy making perspective. Three scenarios are examined to demonstrate the functionality of the model, including the interaction between separate policies targeting the Heat and Power sectors. The results demonstrate how the model can be used to examine policy impacts against a range of metrics including the contribution to renewable energy and carbon reduction targets; cost to the exchequer and the marginal cost of carbon abated. The model has helped to inform the development of a renewable heat policy instrument in Ireland.

An application of the use of safe minimum standards in the conservation of livestock biodiversity

This paper adapts the safe minimum standard (SMS) approach so as to explore its use as a potential policy decision support tool that can be applied to issues related to the conservation and sustainable use of farm animal genetic resource (AnGR) diversity. Empirical SMS cost estimates are obtained using data from three AnGR economics case studies in Mexico and Italy. The findings support our hypothesis that the costs of implementing an SMS are low, both when compared with the size of subsidies currently being provided to the livestock sector (<1 percent of the total subsidy) and with regard to the benefits of conservation (benefit-cost ratio of >2.9).Nevertheless, despite providing a potentially useful AnGR conservation decision support tool, a critical assessment of the application reveals that a much more extensive quantification of the components required to determine SMS costs needs to be undertaken before this tool can be applied in practice.