Citizens’ Valuation of Urban Afforestation as a Function of the Trade-Offs between CO2 Reduction, Co-Benefits and Land Use Conflicts

Last registered on January 19, 2024
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Pre-Trial

Trial Information

General Information

Title
Citizens’ Valuation of Urban Afforestation as a Function of the Trade-Offs between CO2 Reduction, Co-Benefits and Land Use Conflicts
RCT ID
AEARCTR-0012316
Initial registration date
January 15, 2024

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
January 19, 2024, 2:04 PM EST

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

Locations

Country
Germany
Region

Primary Investigator

Name
Lara Bartels
Affiliation
ZEW – Leibniz Centre for European Economic Research
Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2023-10-29
End date
2024-02-28
Keywords
Behavior, Environment & Energy
Additional Keywords
Choice experiment, Stated preferences, Urban afforestation
JEL code(s)
Q51, C93, C83, Q23
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
By 2030, about 60% of the world’s population is projected to live in urban areas. With cities consuming about three-quarters of global natural resources and energy consumption and being responsible for 70% of global CO2 emissions (Gurney et al. 2015; UN-Habitat 2021), they significantly contribute to climate change, but are at the same time most vulnerable to climate change (Rosenzweig et al. 2010). Nature-based solutions such as urban green and blue infrastructures present opportunities for sustainable urbanization, as well as climate mitigation and adaption in urban areas (Martos et al. 2016). Among these, urban forests have the greatest mitigation potential (Lwasa et al. 2022) while also providing substantial co-benefits that facilitate climate adaptation in cities and make living in urban areas more pleasant. Accurately determining the values associated with city trees is crucial for the effectiveness and targeting of tree planting programs (Li 2023) and can ultimately increase a community’s resilience to various climate change-related stresses. To now, there is a limited understanding of the publics' valuation of city trees and the services they provide. To account for this, this study employs a stated choice experiment (CE) to investigate the assessment of diverse urban afforestation programs, aiming to differentiate the values assigned by participants to various aspects of these programs. In particular, the choice experiment enables the differentiation of values attributed by participants to the climate mitigation potential of trees (carbon sequestration) compared to other co-benefits (heat island reduction and increased biodiversity), as well as potential land use conflicts (loss of parking or residential housing space). Therewith, this research fills an important knowledge gap by providing a nuanced understanding of how individuals assess and prioritize diverse aspects of urban afforestation programs. The findings will help to inform sustainable urban planning strategies that balance climate change mitigation, environmental benefits, and potential drawbacks, ultimately leading to more effective and socially acceptable urban afforestation programs.
External Link(s)

Registration Citation

Citation
Bartels, Lara. 2024. "Citizens’ Valuation of Urban Afforestation as a Function of the Trade-Offs between CO2 Reduction, Co-Benefits and Land Use Conflicts." AEA RCT Registry. January 19. https://doi.org/10.1257/rct.12316-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
The objective of this study is to conduct a stated choice experiment to understand the valuation of urban afforestation of German citizens. Specifically, I aim to identify the relative importance of different attributes of urban afforestation, such as the co-benefits of urban trees, land conflicts, location of tree, and carbon sequestration potential and aim to i) identify the attributes of urban afforestation that are most important to urban residents, ii) estimate the economic values that urban residents attach to these attributes, and iii) explore how demographic and socio-economic factors as well as locational variables (i.e. the tree population of the neighborhood) influence choices and can help explain taste heterogeneities.

Study participants will be invited to answer a survey on city climate mitigation and adoption that also included a stated choice experiment on urban afforestation. The survey is structured in four main parts: (i) general introduction with some pre-experimental questions, (ii) introduction to the choice context with some behavioral, context-related questions, (iii) the choice experiment tasks and follow-up questions to the tasks, and (iv) socio-demographic questions. Within the choice experiment, each participants answers six choice sets in which respondents are asked to choose among three alternatives, i.e., two urban afforestation measures (alternative 1 and 2) and a status quo option.
Intervention Start Date
2023-10-29
Intervention End Date
2024-02-28

Primary Outcomes

Primary Outcomes (end points)
Discrete choice experiment among two alternatives (and a status quo option) with six attributes (carbon sequestration, location, local co-benefits, alternative land usage, neigborhood, financial contribution). Each participants answers six choice sets. In each choice set, respondents are asked to choose among three alternatives, i.e., two afforestation measures (alternative 1 and 2) and a status quo option.

Primary Outcome Choice: A variable "choice" takes the value 1 for the chosen alternative and 0 for the alternative 1 or 2 that was not chosen. For the status quo alternative, the dummy variable ‘status quo’ is constructed which takes the value one if the respondent chose the status quo option.

Primary Outcome WTP: I will in addition estimate the mean WTP for each attribute by dividing the estimated mean of the random parameters by the estimated fixed parameter of the attribute "Financial contribution".
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
In addition to the primary outcomes, some sub-group analyses are planned based on the additional included survey items:
- visits to urban green spaces, satisfaction with urban green spaces
- climate attitudes
- economic preferences, such as time, altruism, reciprocity, trust...
- socio-economics such as age, gender, income, car ownership
- financial and political consequentiality

Lastly, the experimental data are planned to be matched with observational data. The Berlin administration provides an extensive database on the state of trees in the city area. This database is differentiated by neighborhoods and informs about the amount of trees in each neighborhood, ii) amount if trees per kilometer of street, iii) distance between trees in meters, iv) share of newly planted trees, v) felling rate, and iv) percentage change in total tree stock. With these data and the great variety, assumptions about decreasing marginal returns for additional urban nature can be tested on a fine granular level.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
I am employing a discrete choice experiment within an online survey to evaluate the valuation of urban afforestation by citizens of a major German city. The survey will be administered by the survey institute Bilendi to a specifically chosen group of participants from their panel database, with a target of 1,000 responses. Participants will be notified about the survey via email, with the added incentive of earning points that can be exchanged for vouchers, cash, or donations.

The sample is restricted to individuals residing in Berlin, ensuring a representative distribution in terms of gender, education (high vs. medium/low), and age to reflect the demographics of the German population.

The final data collection is scheduled for February 2024. A pre-test was conducted in November 2023 with a broader population sample of approximately 260 participants. For the pre-test, a d-efficient design was generated using the Stata command "dcreate" for a multinomial logit model with zero priors for main effects. The priors obtained from the pre-test were subsequently used to estimate a final d-efficient design. The ultimate choice experiment comprises 150 choice sets distributed across 25 blocks. Individuals are randomly assigned to one of the 25 blocks and answer the included six choice sets in a random order.
Experimental Design Details
Randomization Method
Participants are randomly assigned to one of the 25 blocks by the survey software and respond to the included six choice sets in a random order. Order effects will be accounted for in the regression analysis.
Randomization Unit
Individual level.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
No clusters.
Sample size: planned number of observations
Sample Size: 1000 individuals.
Sample size (or number of clusters) by treatment arms
Not applicable.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Not applicable.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

Post-Trial

Post Trial Information

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

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Reports & Other Materials