Data from: Advances and shortfalls in applying best practices to global tree-growing efforts
Data files
Jan 22, 2024 version files 81.76 KB
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README.md
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Schubert.et.al_ConLet_TreeOrgs_11Jan2024.csv
Abstract
As global tree-growing efforts have escalated in the past decade, copious failures and unintended consequences have prompted many reforestation best practices guidelines. The extent to which organizations have integrated these ecological and socioeconomic recommendations, however, remains uncertain. We reviewed websites of 99 intermediary organizations that promote and fund tree-growing projects to determine how well they report following best practices. Nearly half the organizations stated tree or area planting targets, but only 25% had measurable, time-bound objectives. Most organizations discussed the benefits local communities would receive from trees, but only 38% reported measures of these outcomes. Non-profit organizations with greater prior experience converged more closely on best practices, and their level of scientific expertise was positively associated with clearer project selection standards. Although many tree-growing organizations acknowledge the importance of clear goals, local community involvement, and monitoring, our results raise questions regarding whether long-term benefits are being achieved and emphasize the need for stronger public accountability standards.
README: Advances and shortfalls in applying best practices to global tree-growing efforts
https://doi.org/10.5061/dryad.wdbrv15w4
1. Author Information
A. Corresponding Author Contact Information
Name: Spencer C. Schubert
Institution: University of California Santa Cruz
Email: scschubert11@gmail.com
B. Corresponding Author Contact Information (secondary contact)
Name: Karen D. Holl
Institution: University of California Santa Cruz
Email: kholl@ucsc.edu
C. Co-authors
K. E. Battaglia, C. N. Blebea, C. J. P. Seither, and H. L. Wehr
2. Date of data collection
2023-Jan-09 to 2023-Mar-21
3. Geographic location of data collection
compiled from webpages accessed from Santa Cruz, California, USA
4. Information about funding sources:
MacArthur Foundation University of California Endowed Chair (held by KDH)
SHARING/ACCESS INFORMATION
Recommended citation for this dataset
Schubert, Spencer et al. (Forthcoming 2024). Advances and shortfalls in applying best practices to global tree-growing efforts [Dataset]. Dryad. https://doi.org/10.5061/dryad.wdbrv15w4
DATA & FILE OVERVIEW
1. Summary
The data we provide on this Dryad link represent the information extracted from our online survey of 99 websites of intermediary tree-growing organizations. These data -- in addition to characterizing background, administrative structure, geography of organizational activities, etc... -- address an itemized list of prompts expanding upon the ten questions funders should ask about reforestation (Holl and Brancalion 2022). We calculated a Best Practices Index to measure transparency and adherence to these principles based on the sum of an itemized scoring approach (see Schubert et al. 2024 and Supplementary Extended Methods for more details).
2. File List
"Schubert_ConLet_TreeOrgs_11Jan2024.csv" - data file for online survey of 99 selected intermediary tree-growing organizations
3. Date Last Modified
9/25/2023 -- additional data collected upon revision of the manuscript (see Carbon.credits)
METHODOLOGICAL INFORMATION
1. Description of methods used for collection/generation of data (see Schubert et al. 2024 and Supporting Information: Extended Methods for more details)
Data collection occured Jan-Mar 2023. We identified 99 tree-growing intermediary organizations from recently published studies -- Bosshard et al. (2021) and Martin et al. (2021) -- and by searching Google and online charity platforms. These constituted private sector organizations that:
(1) fund tree-growing or forest landscape restoration projects that are implemented by local organizations and landholders in multiple regions (i.e., at least multiple states/provinces within a country) (2) have a web presence with information communicating the organization’s restoration activities to potential funders. We excluded organizations that are primarily funded by government sources that are less dependent on web pages to share information with potential donors, fundraise for other intermediary organizations, or are exclusively service providers for hire (e.g., planted trees as a business). Each organization was assessed for data collection by at least two authors, who followed a protocol with survey prompts designed to collect information pertaining to the ten questions funders should ask about reforestation (Holl and Brancalion 2022). Data collection consisted of recording information to address a combination of categorical and open-ended questions that describe the organizations and their tree-growing practices. All web pages of the organizations primary website, in addition to all publicly-available annual and financial reports corresponding to tree-growing standards were subject to review. These prompt questions are detailed in the Extended Methods section of
Schubert et al. 2024 Supporting Information. Cumulative review times ranged 50–170 min per organization, depending on the extent of information available. We conducted a final search 21–25 March 2023 for new annual reports to ensure we used the most updated information.
2. Persons responsible for data entry and proofing
K. E. Battaglia, C. N. Blebea, C. J. P. Seither, H. L. Wehr, Y. Sheikhvand, and S. C. Schubert
3. Methods for processing the data
The data presented here have been redacted slightly from the original survey spreadsheets for multiple reasons. First, we provide a numeric code 1-99 to identify each organization rather than actual organization names since the goal of the study was to characterize trends rather than evaluate individual organizations. Similarly, some descriptive open-ended columns include lengthy responses, quoted text, and project names/locations which we hold confidential. We have included the data necessary to repeat the quantitative analyses in the study.
From the data initially collected from our online surveys, we calculated an index to evaluate the extent of organizations’ adherence and transparency to best practices based on our expanded questions, hereafter ‘best practices index’. The index was based on a sum of 21 responses with a total possible value of 22 points derived from presence and extent of information provided by organizations (e.g., no information = 0, vague response = 0.5, information provided = 1). We used multiple regression to test whether certain organizational characteristics including organization type (non-profit or for-profit), past experience (prior number of trees or area planted/conserved), and staff scientific expertise predicted the best practices index. To examine relationships between specific ordinal variables, we used Kendall rank correlation. Data processing, operations, analysis, and figure rendering were performed in R 4.3.0 (R Core Development Team 2023). For further information concerning code and additional data collected, send inquiries to Spencer Schubert (scschubert11@gmail.com).
DATA-SPECIFIC INFORMATION FOR: "Schubert.et.al_ConLet_TreeOrgs_11Jan2024.csv"
1. Number of variables: 69
2. Number of cases/rows: 99
3. Overview and Comments on Interpreting Data
Definitions of acronyms and terms used across the data set:
"Y" - yes
"Vague” - This response signifies that whether or not the organization met the criteria for a particular column was inconclusive but plausible. This response was designated for cases in which organizations mentioned or provided brief reference to the subject of the prompt, but provided no detailed information/examples/data that we could further analyze
"N" - no
"NI" - no information
"DOP" - depends on project. All intermediary organizations supported multiple restoration projects and in some cases information was only presented for select projects, indicating that the criteria is met for select projects, but not necessarily universally applied/required for all sponsored projects. “Other summary statistics”: reported numeric figures on other variables such as the number of sites planted, nurseries established, individuals/communities impacted without specifically reference measures of tree-growing parameters. Considered as “Vague” in best-practices index score.
“Engagement only”: the website indicated the number of people or communities impacted by a project without specifying the type or extent of impact (e.g., number of community events, number of people affected). Considered as equivalent to “Vague” in best-practices score
Calculated Columns for Best Practices Index
All columns beginning with Q#. correspond to Table 1 of Schubert et al. 2024 and are organized around the ten questions funders should ask about reforestation (Holl and Brancalion 2022)
4. Variable List: <list variable name(s), description(s), unit(s)and value labels as appropriate for each>
"Intermediary.org.identifier" - Numeric; coded integer representing each organization
"Year.founded" - Numeric; Year organization was founded
"Org.type" - Categorical; Organization model; options: Non-profit, B-Corps/Social Enterprise, For-profit Company
"Registered" - String/Description; Country in which the organization is registered/headquartered
"Staff.number" - Categorical; Number of staff in the organization; options: <10, 11-25, 26-50, 51-75, 76-100, 101-150, 151-200, >200, NI
"Staff.regional" - Categorical; Are personnel or offices permanently established in regions where projects are implemented?; options: Y, DOP, Vague, N, NI
"Geography" - String/Description; Countries in which the organization supports projects & partners. Up to 10 countries listed or, if more than ten, described as global
"Staff.science" - Categorical; Extent of scientific expertise of project staff; options: NI, Some staff have Bachelor’s degree or certification in relevant field, At least one staff with advanced degree (Masters/PhD); Team(s) of designated advisors/experts (Masters/PhD)
"Purpose.for.trees" - String/List; Goals/cited benefits for tree growing. List of all that apply: Air quality, Biodiversity (conservation), Carbon (sequestration), Climate change (other/general), Human wellbeing (other/general), Ecological restoration, Ecosystem services (other/general), Food security, Human livelihood, Recreation, Resiliency, Soil quality, Timber, Water supply OR water quality
"Strategy.for.trees" - String/List; Methods used to grow trees. List of all that apply: Agroforestry, Natural regeneration (assisted), Planting/Transplanting (seedlings), Protecting forests, Seeding, NI
"Native.nonnative" - Categorical; Types of trees planted; options: Native only, Native and non-native, Native and agricultural trees, NI
"Time.bound.objectives" - Categorical; Does the organization have time-bound objectives that are measurable beyond 1 year? options: Y, DOP, Vague, NI
"Reforested.area" - Numeric; Area that an organization claims to have reforested. Units = hectare (ha)
"Protected.area" - Numeric; Area that an organization claims to have protected. Units = hectare (ha)
"Planted.quant" - Numeric; Number of trees that an organization claims to have planted.
"Experience.bin" - Categorical/Ordinal; Logarithmic scale of 1-5 representing an estimate of total trees grown; We determined the relative tree-growing experience of organizations as follows. Intermediary organizations that reported to have grown fewer than 100,000 trees were placed into the lowest category (1). Subsequent categories were scaled logarithmically, extending to the highest category (5) for organizations that planted more than 100,000,000 trees. The equivalent measure for area reforested was 250 hectares (i.e., 800 trees = 1 ha reforested). And the equivalent measure in category 1 for forested area protected (but not actively planted) was 1000 hectares (i.e,, 1 ha reforested = 4 ha protected). We considered that organizations generally require fewer resources to implement forest protection and natural regeneration than to actively plant and maintain sites.
"Project.criteria" - Categorical; Does the organization have explicitly stated criteria or conditions that determine whether to support local projects and partners? options: Y, DOP, Vague, NI
"Land.tenure" - Categorical; Does the organization address the role of land tenure in securing the long-term success of projects? options: Requires, Considers, NI
"Drivers.deforestation" - Categorical; Does the organization describe past or potential drivers of deforestation that would threaten current/future projects? options: Y, DOP, Vague, NI
"Drivers.deforestation.type" - String/List; What drivers of deforestation does the organization reference? List of all that apply: Agriculture, Disease, Development, Firewood, Fires, Fisheries, Insect outbreaks, Livestock ranching, Logging (timber), Mining, Salt production, Severe weather, NI
"Drivers.deforestation.address" - Categorical; Does the organization invoke a strategy or plan of action to minimize deforestation threats? options: Y, DOP, Vague, NI
"Negative.consequences" - Categorical; Does the organization describe potential negative consequences of growing trees? options: Y, DOP, Vague, NI
"Negative.consequences.type" - String/List; What negative consequences of reforestation does the organization reference? List of all that apply: Displacement, Destroying low tree cover native habitat, Increasing albedo, Increasing social conflicts, Loss of income, Reducing biodiversity, Reducing water supply, Spread of invasives, NI
"Negative.consequences.address" - Categorical; Does the organization invoke a strategy or plan of action to minimize negative consequences? options: Y, DOP, Vague, NI
"Community.engagement" - Categorical; To what extent is the involvement of local stakeholders discussed across organization webpages? options: No mention of local stakeholder engagement, Mention local stakeholder engagement vaguely, Mention that projects are locally led but no details, Provide detailed discussion of local stakeholder engagement
"Community.benefits" - String/List; Benefits the organization cites as being provided to local stakeholders across various projects. List of all that apply: Cultural, Ecosystem services, Education, Energy, Food security, Income (financial, employment), Social equity (indigenous, gender), Tools (technology), Water, Wellness (medicine, lifestyle improvement, recreation)
"Community.goals" - Categorical; Does the organization describe how local communities contribute to setting project goals? options: Y, DOP, Vague, NI
"Community.planning" - Categorical; Does the organization describe how local communities contribute to planning how and where projects will be developed? options: Y, DOP, Vague, NI
"Community.implementation" - Categorical; Do locals participate as employees or volunteers in the initial establishment of projects? options: Y, DOP, Vague, NI
"Community.maintainence" - Categorical; Does the organization describe how locals are involved in maintaining conditions after initial project establishment? options: Y, DOP, Vague, NI
"Community.monitoring" - Categorical; Does the organization describe how locals participate in monitoring project outcomes in the years after establishment? options: Y, DOP, Vague, NI
"Reports" - Categorical; What reports are available on the organization’s website? options: Annual reports, Financial reports, Both, None
"Past.data" - Categorical; Are quantitative data available for past projects? (other than planting/area totals). options: Y, DOP, Other summary statistics, Vague, NI
"Community.benefits.data" - Categorical; Are data available for outcomes concerning stated benefits to local communities? options: Y, DOP, Engagement only, Vague, NI
"Monitoring.discussed" - Categorical; Does the organization provide information about protocols that will be used to collect data and evaluate outcomes in the years after initial project establishment? options: Y, DOP, Vague, NI
"Tree.survival" - Categorical; Are data available on the percentages of tree survival at project sites? options: Y, DOP, Vague, NI
"Tree.survival.per" - Numeric; Percent tree survival reported. In cases where multiple survival percentages were available, a single midpoint or mean value is presented.
"Monitoring.years" - Numeric; How many years after initial establishment are projects monitored?
"Maintenance.years" - Numeric; How many years after initial establishment are projects cared for?
"Follow.up.bin" - Categorical/Ordinal; Binned numeric category of maintenance or monitoring commitment in years. options: < 1 OR Unclear, 1-5, 5-10, > 10 years
"Maintainence.responsible" - Categorical; Who is responsible for project maintenance? options: Intermediary staff, Implementing staff, Local landholders, Third party (e.g., park staff), NI
"Cost.split" - Categorical; Does the organization provide information about the proportion of funding dedicated to local tree growing projects versus implementing organization administrative costs? options: Y, DOP, Vague, NI
"Cost.split.programs" - Numeric; Percentage value of funding that goes to local programs.
"Cost.split.maintenance" - Categorical; Is part of the funding to local programs explicitly designated for maintenance activities? options: Y, DOP, Vague, NI
"Cost.split.landowners" - Categorical; Is part of the funding to local programs explicitly devoted to compensating landholders? options: Y, DOP, Vague, NI
"Funding.committment.bin" - Categorical/Ordinal; Binned numeric category of funding commitment in years. options: < 1 OR Unclear, 1-5, 5-10, >10
"Carbon.credits" - Categorical; Does the organization sell carbon credits as part of its revenue model? options: Y, Vague, NI
"Q1.purpose.a" - Numeric; Goal(s) for Tree-Growing; Identifies at least one goal that tree-growing addresses; calculated score: value = 1 if TRUE
"Q1.purpose.b" - Numeric; Goal(s) for Tree-Growing; Identifies objectives that are time-bound and measurable beyond one year; calculated score: value = 1 if TRUE
"Q1.purpose.c" - Numeric; Goal(s) for Tree-Growing; Specifies criteria used to select and support local projects; calculated score: value = 1 if TRUE
"Q2.strategy.a" - Numeric; Strategies for Tree-Growing; Specifies a strategy to grow trees; calculated score: value = 1 if TRUE
"Q2.strategy.b" - Numeric; Strategies for Tree-Growing; Discusses whether native vs. nonnative trees are planted; calculated score: value = 1 if TRUE
"Q3.drivers.a"- Numeric; Addresses Drivers of Deforestation; Identifies original drivers of deforestation in project region(s); calculated score: value = 1 if TRUE
"Q3.drivers.b" - Numeric; Addresses Drivers of Deforestation; Reports measures taken to mitigate destruction/degradation of projects; calculated score: value = 1 if TRUE
"Q4.locals" - Numeric; Local Community Engagement; Extent to which local stakeholder involvement is discussed; calculated score: detailed discussion = 2,
mention projects locally led = 1, mention vaguely = 0.5
"Q5.local.benefits.a" - Numeric; Local Benefits; Identifies project benefits to local communities/stakeholders; calculated score: value = 1 if TRUE
"Q5.local.benefits.b" - Numeric; Local Benefits; Measures project benefits to local communities/stakeholders; calculated score: Yes = 1, Engagement Only, Vague = 0.5
"Q6.negatives.a" - Numeric; Minimize Negative Consequences; Identifies potential negative consequences of tree-growing to people and the environment; calculated score: value = 1 if TRUE
"Q6.negatives.b" - Numeric; Minimize Negative Consequences; Reports measures taken to mitigate negative consequences; calculated score: value = 1 if TRUE
"Q7.maintenance.a" - Numeric; Maintenance; Reports the length of time that projects are maintained beyond initial implementation; calculated score: value = 1 if TRUE
"Q7.maintenance.b" - Numeric; Maintenance; Identifies who is responsible for maintaining project sites; calculated score: value = 1 if TRUE
"Q8.monitoring.a" - Numeric; Monitoring; Discusses monitoring protocol; calculated score: value = 1 if TRUE
"Q8.monitoring.b" - Numeric; Monitoring; Reports the length of time that projects are monitored; calculated score: value = 1 if TRUE
"Q8.monitoring.c" - Numeric; Monitoring; Specifies the project parameters that will be measured; calculated score: value = 1 if TRUE
"Q9.outcomes.a" - Numeric; Outcomes of Prior Projects; Reports quantitative data on number of trees or area reforested/protected; calculated score: value = 1 if TRUE
"Q9.outcomes.c" - Numeric; Outcomes of Prior Projects; Reports tree survival/mortality or other quantitative data (e.g., CO2 sequestered, tree cover estimates, local biodiversity) from past project monitoring; calculated score: Yes = 1, Other summary statistics = 0.5, Vague = 0.5
"Q10.funding.a" - Numeric; Funding; Provides information about the length of time that projects are funded; calculated score: value = 1 if TRUE
"Q10.funding.b" - Numeric; Funding; Provides information about the percentage of costs that are allocated to intermediary organizations vs. tree-growing projects in the field; calculated score: value = 1 if TRUE
"score" - Numeric; Best Practices Index; calculated as the sum of Q1-Q10 columns; total possible score = 22
Methods
1. Description of methods used for collection/generation of data (see Schubert et al. 2024 and Supporting Information: Extended Methods for more details): Data collection occured Jan-Mar 2023. We identified 99 tree-growing intermediary organizations from recently published studies -- Bosshard et al. (2021) and Martin et al. (2021) -- and by searching Google and online charity platforms. These constituted private sector organizations that: (1) fund tree-growing or forest landscape restoration projects that are implemented by local organizations and landholders in multiple regions (i.e., at least multiple states/provinces within a country) (2) have a web presence with information communicating the organization’s restoration activities to potential funders. We excluded organizations that are primarily funded by government sources that are less dependent on web pages to share information with potential donors, fundraise for other intermediary organizations, or are exclusively service providers for hire (e.g., planted trees as a business). Each organization was assessed for data collection by at least two authors, who followed a protocol with survey prompts designed to collect information pertaining to the ten questions funders should ask about reforestation (Holl and Brancalion 2022). Data collection consisted of recording information to address a combination of categorical and open-ended questions that describe the organizations and their tree-growing practices. All web pages of the organizations primary website, in addition to all publicly-available annual and financial reports corresponding to tree-growing standards were subject to review. These prompt questions are detailed in the Extended Methods section of Schubert et al. 2024 Supporting Information. Cumulative review times ranged 50–170 min per organization, depending on the extent of information available. We conducted a final search 21–25 March 2023 for new annual reports to ensure we used the most updated information.
2. Persons responsible for data entry and proofing: K. E. Battaglia, C. N. Blebea, C. J. P. Seither, H. L. Wehr, Y. Sheikhvand, and S. C. Schubert
3. Methods for processing the data: The data presented here have been redacted slightly from the original survey spreadsheets for multiple reasons. First, we provide a numeric code 1-99 to identify each organization rather than actual organization names since the goal of the study was to characterize trends rather than evaluate individual organizations. Similarly, some descriptive open-ended columns include lengthy responses, quoted text, and project names/locations which we hold confidential. We have included the data necessary to repeat the quantitative analyses in the study. From the data initially collected from our online surveys, we calculated an index to evaluate the extent of organizations’ adherence and transparency to best practices based on our expanded questions, hereafter ‘best practices index’. The index was based on a sum of 21 responses with a total possible value of 22 points derived from presence and extent of information provided by organizations (e.g., no information = 0, vague response = 0.5, information provided = 1). We used multiple regression to test whether certain organizational characteristics including organization type (non-profit or for-profit), past experience (prior number of trees or area planted/conserved), and staff scientific expertise predicted the best practices index. To examine relationships between specific ordinal variables, we used Kendall rank correlation. Data processing, operations, analysis, and figure rendering were performed in R 4.3.0 (R Core Development Team 2023). For further information concerning code and additional data collected, send inquiries to Spencer Schubert (scschubert11@gmail.com).