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Tabea Lissner is a Research Director of the Global Solutions Initiative Foundation in Berlin. Followed by a panel on The Global Adaptation Tracking Initiative – challenges & opportunities
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Tabea Lissner is a Research Director of the Global Solutions Initiative Foundation in Berlin. Followed by a panel on The Global Adaptation Tracking Initiative – challenges & opportunities
Panelists: Tabea Lissner (Global Solutions Initiative), Chandni Singh (Indian Institute for Human Settlements), Portia Adade Williams (ASCEND; CSIR-STEPRI), Ken Chomitz (Global Innovation Fund) Moderator: Jenn Thornhill Verma (McMaster Health Forum)
To contribute to Paris Agreement objectives, non-state actors globally are increasingly developing voluntary emission reduction goals. Cities represent an important group with net-zero or similar plans, but significant variation exists in how they intend to meet these targets. A lack of research exists on the role of carbon offsetting (carbon dioxide removal or avoided emissions […]
Prevalent systems of land transport, centred around automobility, are energy inefficient and involve high economic, health and GHG emissions-related costs. As one class of solutions, demand-side behavioural change interventions (or soft policy interventions) are promoted to reduce car use and shift travellers to low-carbon transport modes. However, the relative effectiveness of such interventions in initiating […]
Reducing emissions from the building sector is one of the key steps in combating climate change, as the building sector is responsible for about 38% of global greenhouse gas emissions. Private households have opportunities to reduce a significant proportion of these emissions by improving the energy efficiency of their buildings, e.g. by refurbishing their insulation […]
This paper conducts a meta-analysis on rebound effects regarding the energy transition for household end-use services toward renewable energies (including photovoltaic, biomass, green electricity, and heat pumps). The biomass and heat pump potentially lead to the highest size of rebound effect (approximately 33%). In addition, the rebound effect regarding green electricity is about 1–3 % […]
Behavioral interventions are considered promising approaches to promote pro-environmental behavior. Applying these interventions in high-emission sectors such as transportation could help mitigating climate change. While systematic evidence synthesis is important for a variety of stakeholders within and beyond academia, to our knowledge there are few systematic reviews and no systematic map focusing on behavioral interventions […]
Researchers have incentives to search for and selectively report findings that appear to be statistically significant and/or conform to prior beliefs. Such selective reporting practices, including p-hacking and publication bias, can lead to a distorted set of results being published, potentially undermining the process of knowledge accumulation and evidence-based decision making. We take stock of […]
Meta-analysis upweights studies reporting lower standard errors and hence more precision. But in observational settings, precision is not given to the researcher. Precision must be estimated, and thus can be p-hacked to achieve statistical significance. Simulations and applications show that spurious precision can invalidate inverse-variance weighting and bias-correction methods based on the funnel plot. Selection […]
CO2 pricing via taxes or emission trading schemes (ETS) should create incentives for companies and individuals to reduce their carbon footprint. We exploit a large meta-dataset of price elasticities for heating and cooling energy sources. We compare elasticities from both state-led and market-led changes, controlling for study-design characteristics. Our working hypothesis is that taxes and […]
We follow a registered pre-analysis plan (\href{https://osf.io/zdche}{https://osf.io/zdche}) to create a large meta-dataset from the international literature on price elasticities of energy demand for heating and cooling of households and businesses. The dataset comprises 442 primary studies with more than 4971 price elasticity estimates, their standard errors as well as relevant study and observation characteristics. We […]
Meta-analyses in economics frequently exhibit considerable overlap among primary samples. If not addressed, sample overlap leads to efficiency losses and inflated rates of false positives at the meta-analytical level. Bom and Rachinger (2020) propose a generalized-weights approach to handle sample overlap. This approach effectively approximates the correlation structure between primary estimates using information on sample […]