The Demand for Voluntary Carbon Sequestration – Experimental Evidence from a Reforestation Project in Germany

Last registered on December 01, 2020


Trial Information

General Information

The Demand for Voluntary Carbon Sequestration – Experimental Evidence from a Reforestation Project in Germany
Initial registration date
December 01, 2020

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
December 01, 2020, 1:24 PM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.



Primary Investigator

ZEW – Leibniz Centre for European Economic Research

Other Primary Investigator(s)

PI Affiliation
University of Muenster
PI Affiliation

Additional Trial Information

On going
Start date
End date
Secondary IDs
A deeper understanding of individual behavior that determines the voluntary provision of environmental public goods is of special importance for climate action where decentralized informal institutions like societal norms and values gain momentum. For achieving the 2°C global temperature target, IPCC scenarios suggest net-zero emissions by around 2050. So far, the experimental literature on individual climate change efforts focuses on the underlying individual willingness-to-pay for CO2 mitigation. As novelty, we investigate voluntary contributions to a negative emission technology in form of a local forest carbon sink. We conduct a framed-field experiment highlighting not only the climate impact, but also the role of local ancillary benefits resulting from the provision of a local forest carbon sink and the importance of the spatial dimension.
External Link(s)

Registration Citation

Bartels, Lara, Martin Kesternich and Andreas Löschel. 2020. "The Demand for Voluntary Carbon Sequestration – Experimental Evidence from a Reforestation Project in Germany." AEA RCT Registry. December 01.
Experimental Details


Our study exploits the demand for voluntary carbon sequestration in form of a reforestation project in Germany. For the intervention, customers of an online comparison platform for electricity tariffs that operates all over Germany are invited to participate in a survey thematically unrelated to the experiment. After completing the online survey, participants were, for the first time, confronted with the opportunity to donate their fixed payment earned in the survey (20€) to a carbon sink project. With the help of an adjustable slider, they could determine if and how much of their fixed payment they wanted to donate. Unknown to the potential donors, subjects were at this stage randomly allocated to one out of the two treatments which only varied in its information about the carbon sinks project.
The first wave of interventions (with the two treatments, sinks (S) and co-benefit sinks (CBS)) took place from 16th to 25th March 2020. A second wave of interventions (with the same two treatments as in the first wave) is planned within the first half of 2021.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
As primary outcome measures, we analyze the willingness to contribute to the sequestration project in form of a donation decision. We will investigate both extensive margin (i.e. share of donors) and intensive margin (i.e. amount of money donated) effects.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Subjects take part in an online survey. After completing the survey they are given the opportunity to donate their fixed payment earned in the survey to a carbon sink project. With the help of an adjustable slider, they could determine if and how much of their fixed payment (20 €) they want to donate.

Participants are randomly allocated to two information treatments, which vary in its information about the carbon sinks project. The sinks (S) treatment gives information on the need for global climate protection and the role of carbon sinks within this process. Also, information on the average CO2 absorption capacities of trees were given and put in reference to the emissions value of a car trip. Then, subjects were asked if and how much they would like to donate for the removal of 100kg CO2 from the atmosphere through a specified afforestation project. The second treatment, the co-benefit sinks (CBS) treatment, contained all the information of the S treatment but also included an extra part that additionally to the CO2 removal stressed the local ancillary benefits from the reforestation project.

Our main hypotheses (H1) is that if subjects value the local benefits adequately we would be able to reject our Null-Hypothesis (H0: WTPCBS = WTPS). Given the variety in places of residence of our subjects, we expect a difference between those who are located close to the reforestation project (and thus reveal some additional local benefits for themselves from planting trees) and those who are located further away. If this holds we would reject the Null-hypothesis (H0: ρ(WTPCBS,d)=0) in our hypothesis H3 which captures the correlation ρ between the physical distance d to the reforestation project and the willingness to pay.
Experimental Design Details
The selected reforestation project is part of the Bundesgartenschau (Federal Horticultural Show) in 2023. The City of Mannheim has been selected to host the Bundesgartenschau in 2023. The Bundesgartenschau is a German exhibition on horticulture which also included topics such as landscape architecture. In its current form, it traces back to 1951 and takes place every two years in various German cities, and every ten years as the International Horticultural Exhibition. In 2019, the Federal Horticultural Show took place in Heilbronn and attracted a total of around 2.3 million visitors on the 173 open days (Statista, 2019). As part of the preparations for the upcoming event, the City of Mannheim plans to unseal urban areas and create an additional local carbon sink by permanently planting about 1.000 trees. Participants were informed that they were able to donate to the planting of additional trees within the Bundesgartenschau.
Randomization Method
Participants are randomly allocated to the S and CBS treatment by a computer based random number generator mechanism.
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
The treatments are not clustered.
Sample size: planned number of observations
Within the first intervention wave a total of 160 participants took part in the survey. The second intervention wave shall also contain 160 observations.
Sample size (or number of clusters) by treatment arms
Participants are randomly allocated to the treatments, with an equal weighting of the treatments. In the first round, we have 73 observations in the S-treatment; in the CBS-treatment we have 87 observations. The same randomization will be applied in the second intervention wave.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We base our optimal sample size calculations sizes using results from the related experimental study conducted by Löschel et al. (2021) using a Chinese sample. They report an extensive margin effect of -31% when turning from the local (i.e., Beijing, where 66% of the subjects contribute) to the global setting (i.e., Shenzen, 44% of the subjects contribute). To be able to detect a similar effect size, a power analysis with an underlying two-sample proportions (Pearson’s χ2) test (with α=0.05, p1=0.66, p2=0.44 indicates) that at least 150 experimental observations are needed to achieve a statistical power of 0.7. We were able to recruit 160 subjects for our experiment. After the first wave of interventions, the following patterns emerge. Our preliminary results clearly indicate that the share of subjects that contribute to CO¬2 removal is larger than zero in both treatments (t-test, p=0.000). 65.0% of all subjects in our sample contribute a positive amount to the public good. While 70.0% of all subjects give a positive amount in S, this share decreases to 60.9% in CBS. This decrease is however not statistically significant at any conventional level (exact Fisher´s test, p=0.249). In the following, we now describe subjects’ implicit willingness to pay (WTP) for CO2 removal. We denote the amount of money that a subject contributes to the reforestation project as its minimum WTP (WTPmin). Including all observations, the median WTPmin is 4.55 EUR/100kg CO2 removal and the mean WTPmin amounts to 6.33 EUR/100kg removal. Analyzing differences between treatments we find that both the median WTPmin (5 EUR in S vs. 2 EUR in CBS, MW-test, p=0.1449) and the mean WTPmin (7.18 EUR in S vs. 5.61 EUR in CBS, t-test, p=0.1663) does not differ significantly between S and CBS in our sample. In relative terms, average contributions amount attribute to 35.9% (in S) and 28.1% (in CBS) of the initial enumeration (20 EUR) and are therefore a little bit smaller than average contribution levels in conventional one-shot public goods experiments. Looking only at the contributions of positive contributors, we again find that both the median WTPmin (10 EUR in S vs. 10 EUR in CBS, MW-test, p= 0.3887) and the mean WTPmin (10.28 EUR in S vs. 9.21 EUR in CBS, t-test, p= 0.4200) does not differ significantly between S and CBS in our sample. Most notably, however, these first insights already indicate that a change in the treatments from a pure CO2 perspective to a scenario where local ancillary benefits from CO2 removal are explicitly stressed do not lead to a higher WTPmin. We can therefore not reject the Null-Hypothesis (H0: WTPCBS = WTPS) of H2. Given our sample size calculations we are confident that this the Null result is not driven by an underpowered sample. We will run a further round of S and CBS interventions in 2021 to further increase the statistical power. In addition, we will control for potential income shocks caused by the COVID-19 pandemic which hit Germany after the first wave of interventions.

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