The influence of collective action on the demand for voluntary climate change mitigation in hypothetical and real situations

In this experiment, we investigate determinants of the individual demand for voluntary climate change mitigation. Subjects decide between a cash prize and an allowance from the EU Emissions Trading Scheme for one ton of CO2 that will be deleted afterwards. We vary the incentives of the decision situation in which we distinguish between real monetary incentives and a hypothetical decision situation with and without a cheap talk script. Furthermore, decisions were implemented either as purely individual or as a collective action using majority voting. We observe a significant hypothetical bias in the demand for voluntary climate change mitigation. In case of the individual decision situation this bias is caused solely by subjects with low income. Collective decision making affects demand positively in the hypothetical decision situation only

Evaluations of agri-environmental schemes based on observational farm data: The importance of covariate selection

Evaluations of agri-environmental schemes (AES) based on observational farm data generally use a matching algorithm for comparing participating and non-participating farms. To mitigate the potential post-matching covariate imbalances between groups resulting from the use of large covariate sets, this paper proposes a method mix that reduces the covariate set and maximises the utilised number of observations. We test the approach on an evaluation of the European Union’s AES in the programming period of 2000–2006, estimating the impacts of AES participation on typical measures of land management, i.e. fertiliser and plant protection expenditures and grassland share. We use Mahalanobis distance matching with exact matching on the entry year of the participating farms and kernel matching with automated bandwidth selection to maximise the utilised sample and increase the estimator’s efficiency. Combining cause-and-effect path analysis with statistical covariate selection algorithms reduces the covariate set and improves balance on the characteristics that describe the production environment, farming intensity, productivity, and farmers’ preferences. We find that AES generate moderate decreases in plant protection expenditure and moderate increases in grassland shares. We conclude that our proposed method mix ensures an efficient use of information and improves the reliability of AES impact evaluation

Data of a willingness to pay survey for national climate change mitigation policies in Germany

The dataset includes responses from a contingent valuation study about the national climate change mitigation policies in Germany. The online survey was carried out in the spring of 2014. It assesses the willingness to pay for an increase of the national CO2 reduction target by 10 percentage points, which closely represents Germany׳s climate change mitigation strategy. Respondents were randomly allocated to one of the following three question formats: The dichotomous choice referendum, the dissonance minimizing referendum and the two-sided payment ladder. The data can be used to investigate the influence of alternative statistical approaches on the willingness to pay measures and their comparison across question formats. Keywords: Willingness to pay, Climate change mitigation, Contingent valuatio

Convergent validity in contingent valuation: an application to the willingness to pay for national climate change mitigation targets in Germany

This stated preferences survey determines the willingness to pay (WTP) for climate change mitigation policies using a representative sample of the German population. WTP is compared across three valuation question formats in a split sample design: the dichotomous choice (DC) referendum, the dissonance minimizing (DM) referendum and the two-way payment ladder (TWPL). The influence of multinational cooperation on WTP is assessed by variation in the hypothetical scenarios. We demonstrate that the DM referendum and the TWPL, two question formats that induce similar response incentives, yield equal mean WTPs. Multinational cooperation did not change WTP in any of the question formats. Implications for current contingent valuation practice are discussed

Agricultural policy evaluation with large-scale observational farm data: Environmental impacts of agri-environmental schemes

Agri-environmental schemes (AES) target at improving environmental status of cultivated land by remunerating farmers willing to commit to higher environmental standards. Thus far, no consensus exists whether AES incentivize adoption of pro-environmental production or simply offer windfall profits for those already operating at lower intensities. Using farm-level data, evaluation typically rests on comparing farms with and without AES. For differencing out unobservables that drive farmers into AES participation and therefore confound impact measurement, DID-matching methods are widespread, yet critical reflection remains sparse. We target at closing this gap by shedding light on the implicit assumptions about cause and effect paths linking participation and treatment outcome. We discuss challenges for identification of causal effects in presence of unobservable confounders over a broad range of methods and illustrate DID methods to estimate AES effects on land-use in West Germany

THE HYPOTHETICAL FREE-RIDER DEFICIT IN THE DEMAND FOR FARM ANIMAL WELFARE LABELED MEAT

This contingent valuation survey examines the stated demand for a farm animal welfare label in Germany. Demand is assessed in an individual and a collective decision framing. The collective decision framing has significant influence on stated demand and the probability to choose labelled meat products, indicating the possibility of free rider behavior by meat consumers. Furthermore, the study reveals that determinants for the choice of labelled meat vary across decision situation. For example, while normative demeanor is important in both frames, expected better taste is important in the individual purchase situation and income is important in the collective decision situation also after controlling for normative attitudes

Counterfactual evaluation of two Austrian agri‐environmental schemes in 2014–2018

Abstract This article investigates the causal effect of farm participation in two Austrian agri‐environmental schemes (AES), Immergrün ( ground cover ) and Zwischenfrucht ( catch cropping ), on fertilizer and plant protection expenditures in the 2014 programming period. Combining European Farm Accountancy Data Network data with information on scheme participation from administrative control data offers identifying farm participation in specific schemes targeted at reducing input intensity. Given the overall small sample, we maximized the utilizable sample size by combining difference‐in‐difference and kernel matching with automated bandwidth selection. To address the remaining post‐matching covariate imbalances, we used double machine learning (DML) techniques for a guided selection of potential confounding covariates. Our results suggest that, given the available sample, we cannot substantiate moderate effects of AES participation, and that guided covariate selection by DML offers no gain over non‐guided covariate selection for the small sample. Our results underline the need to increase the number of farms and the duration in available farm panels to substantiate future counterfactual‐based evaluations of policy