Citizen monitoring in environmental disclosure: An economics perspective.

Criticism is mounting that market-led and state-led initiatives for environmental impact disclosure are too limited in scope and that they rest on too strong assumptions about the quality and impartiality of monitoring and enforcement, with resulting insufficient effect on environmental sustainability. It has been proposed that citizen monitoring may contribute to counteract this void. However, to our knowledge, policy analysis in general and economics in particular has not paid much attention to this role of citizen monitoring. This paper aims to bridge that gap from an economics lens, by exploring the dynamics of disclosing local environmental impact and the potential role of citizen monitoring in environmental policy. To this end, the paper addresses monopolistic versus pluralistic environmental disclosure, letting citizen monitoring represent the latter. The study uses the mining industry as an illustrative case, because of that sector's particular transparency challenges in international value chains, typically with strong negative local environmental impact. It is shown how pluralistic information provision such as citizen monitoring can contribute to incentivizing more reliable information provision, especially in countries with weak state institutions, which is particularly important in the case of high-risk environmental impact. The findings should be of use for shaping environmental policy, providing valuable insights for both policymakers and scholars.

COVID-19 pandemic waves: Identification and interpretation of global data.

The mention of the COVID-19 waves is as prevalent as the pandemic itself. Identifying the beginning and end of the wave is critical to evaluating the impact of various COVID-19 variants and the different pharmaceutical and non-pharmaceutical (including economic, health and social, etc.) interventions. We demonstrate a scientifically robust method to identify COVID-19 waves and the breaking points at which they begin and end from January 2020 to June 2021. Employing the Break Least Square method, we determine the significance of COVID-19 waves for global-, regional-, and country-level data. The results show that the method works efficiently in detecting different breaking points. Identifying these breaking points is critical for evaluating the impact of the economic, health, social and other welfare interventions implemented during the pandemic crisis. Employing our method with high frequency data effectively determines the start and end points of the COVID-19 wave(s). Identifying waves at the country level is more relevant than at the global or regional levels. Our research results evidenced that the COVID-19 wave takes about 48 days on average to subside once it begins, irrespective of the circumstances.

Setting development goals using stochastic dynamical system models.

The Millennium Development Goals (MDG) programme was an ambitious attempt to encourage a globalised solution to important but often-overlooked development problems. The programme led to wide-ranging development but it has also been criticised for unrealistic and arbitrary targets. In this paper, we show how country-specific development targets can be set using stochastic, dynamical system models built from historical data. In particular, we show that the MDG target of two-thirds reduction of child mortality from 1990 levels was infeasible for most countries, especially in sub-Saharan Africa. At the same time, the MDG targets were not ambitious enough for fast-developing countries such as Brazil and China. We suggest that model-based setting of country-specific targets is essential for the success of global development programmes such as the Sustainable Development Goals (SDG). This approach should provide clear, quantifiable targets for policymakers.