Assessing the Feasibility and Socioecological Benefits of Climate-Smart Practices at the Watershed Scale
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1. Introduction
2. Materials and Methods
In three study cases, biophysical modeling at the watershed scale determined the potential co-benefits regarding carbon sequestration and landscape connectivity. The biophysical analysis was performed in two contrasting scenarios: a scenario with an ILM approach (ILM scenario, hereafter) that included three practices (live fences, isolated trees in pastures, and riparian vegetation recovery) and a reference or business-as-usual scenario (BAU scenario, hereafter), implemented in crops and pastures. Subsequently, we included the biophysical information in a feasibility analysis of those practices. Then, seven additional climate-smart practices with no available biophysical data were evaluated economically and financially. In the 10 cases, the feasibility analysis was performed through a cost-benefit analysis, and the benefits of the ILM and BAU scenarios were assessed in three periods (2022, 2026, and 2041).
2.1. Study Area
Ameca-Mascota covers 2745 km2 in Jalisco state, draining to the Pacific coast. Del Carmen spans 16,008 km2 in Chihuahua state, near the USA border. Jamapa extends over 3921 km2 in Veracruz state, draining to the Gulf of Mexico coast.
Natural vegetation cover in Ameca-Mascota and Del Carmen is over 70%, while Jamapa is predominantly used for agriculture and cattle ranching, with over 80% anthropic land use.
There is a range of primary productive activities across the sites based on biophysical conditions. Ameca-Mascota’s topography determines the concentration of activities in the upper watershed valleys and the urban areas in the coastal floodplain. Del Carmen experiences an extreme climate and limited water availability, restricting activities to low-density cattle ranching in extensive areas. In turn, Jamapa has favorable conditions for a range of activities, including coffee agroforestry and cattle ranching, with a dispersed human population.
2.2. Selected Climate-Smart Practices
2.3. Economic Feasibility and Socioecological Co-Benefits of Selected Practices
The co-benefits of the selected practices regarding carbon sequestration and landscape connectivity were first determined with biophysical modeling when possible, and the results were incorporated in the financial and economic analyses. Then, a cost-benefit analysis was conducted to evaluate the implementation feasibility of all of the practices.
2.3.1. Biophysical Analysis: Carbon and Functional Connectivity Co-Benefits
2.3.2. Economic Valuation
2.3.3. Economic Feasibility: Cost-Benefit Analysis
2.4. BAU and ILM Scenarios Definition and Assessment
3. Results
3.1. Carbon and Landscape Connectivity
The carbon values decreased in Ameca-Mascota and Del Carmen watersheds but increased in Jamapa between 2002 and 2018. Deforestation scattered in the Ameca-Mascota watershed was the leading cause of carbon loss. In contrast, Del Carmen experienced a significant decrease in carbon due to the deforestation of a patch of pine-oak forest. In Jamapa, the carbon increased due to mangrove and cloud forest restoration efforts and the passive restoration of forests in abandoned crops.
3.2. Social and Market Costs of Carbon Sequestration and Landscape Connectivity Value
3.2.1. Carbon Sequestration
The social cost of carbon was USD 77 million in Ameca-Mascota, USD 277 million in Del Carmen, and USD 97 million in Jamapa. In contrast, the market value was USD 45 million in Ameca-Mascota, USD 161 million in Del Carmen, and USD 56 million in Jamapa.
3.2.2. Landscape Connectivity
3.3. BAU and ILM Scenarios
According to the Markov chains analysis, the Ameca-Mascota watershed will experience significant natural vegetation losses of 17% and 19% in 2026 and 2041. In contrast, Jamapa, which had less than 20% natural vegetation cover in 2022, is expected to lose only 0.25% and 0.4% in 2026 and 2046. Similarly, Del Carmen will see 0.17% (2026) and 0.21% (2046) transformations of natural vegetation into different land uses.
Effect of the Evaluated Practices on Landscape Connectivity and Carbon Sequestration
3.4. Economic Valuation
The ILM scenario was evaluated from private and social points of view. Regarding the private standpoint, the analysis suggests that most ILM practices positively impact income and costs for livestock and/or agroforestry production in the medium and long term. In addition, the sensitivity analysis showed that having a low interest rate is preferable to having a higher return on investment from the private standpoint. From the social perspective, a low rate implies a higher value of the carbon services and landscape connectivity in the medium and long term. The main results of the private (CBA-P) and social (CBA-S) cost-benefit analyses and the sensitivity analysis of the CBA-P and CBA-S are presented below.
3.4.1. CBA-Private
3.4.2. CBA-Social
4. Discussion
The present study explored the feasibility from financial and economic perspectives of 10 climate-smart practices across different time horizons and watersheds. The socioecological co-benefits regarding enhanced landscape connectivity and carbon services were included in the feasibility analysis of three practices (live fences, the restoration of riparian vegetation, and isolated trees in pastures). The main results showed that most of the 10 practices were feasible in the medium and long term from the private and social standpoints. Also, combining the three biophysically modeled practices enhanced carbon services and functional connectivity at the landscape scale, with different performances depending on the case study and the implementation intensity.
The following sections discuss the main findings, considering the socioecological benefits and the utility of CBA in assisting decision-making and supporting the design of grounded policies in priority regions.
4.1. Co-Benefits of Practices with Short-Term Feasibility
4.2. Co-Benefits Provided by Practices with Medium- and Long-Term Feasibility
4.3. The Unfeasible Practices
4.4. The Importance of the CBA
CBA enabled the identification of mutually beneficial practices that enhance producers’ income while simultaneously aiding in the restoration of landscape connectivity and carbon ecosystem services. CBA, preferably supported by biophysical modeling, allows for identifying feasible practices for producers, considering their financial valuation and payback times. Additionally, CBA allows for the economic valuation of the societal benefits of the implemented practices, aiding in decision-making, resource allocation, and policy formulation. Moreover, CBA enables the promotion of conservation efforts by considering ESs recovery, such as the habitat quality measured through landscape connectivity and carbon sequestration and storage. Visualizing the economic feasibility and payback times of various actions is particularly significant when intervention is needed in large areas with limited technical expertise and financial resources.
4.5. Public and Private Feasibility Analysis into Public Policy
Given Mexico’s vast biodiversity and cultural richness, therefore having diverse conservation interests, it is critical to include monetary aspects and biophysical data or information to prioritize its investments and design effective conservation and development policies. The incorporation of monetary aspects into ESs conservation and sustainable-use projects requires considering investments, costs, and revenues that directly impact beneficiaries, integrating all social benefits and positive externalities, which can be measured in monetary terms.
Allocating a larger budget to ESs conservation and recovery is of the utmost importance for public policy in the face of climate change, whether at the national, state, or municipal level. In order to accomplish this, authorities responsible for environmental and production matters can utilize the findings of feasibility and co-benefits studies to support their case and advocate for the maintenance of or increasing public funds as well as the design of grounded policies in priority regions. By presenting the results of these studies in monetary terms in addition to the ecological aspects, negotiations with the public financial sector, including finance ministries, can be more effectively conducted. This integrated approach allows for a comprehensive understanding of the economic value associated with ESs, providing a persuasive argument for budgetary allocations that prioritize their conservation and social benefits.
4.6. Methodological Caveats
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Future scenarios: Future scenarios have limitations as they rely on assumptions and past trends, excluding unmanifested trends and unimplemented policies. Despite these limitations, future scenarios provide insights into favorable and unfavorable impacts;
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Data source heterogeneity and lack of field data information: Modeling outcomes depend on the scale of the input data, and this study used a watershed scale to show significant landscape patterns that may directly affect the provision of ESs in priority regions. Further efforts are needed to understand localized effects and improve the accuracy by incorporating field data;
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Carbon sequestration and storage methods: The InVEST carbon model was chosen for estimating carbon storage and sequestration at the watershed scale. The model is widely accepted internationally and is suitable for our purposes. However, there are limitations to the model, such as assumptions about carbon transitions, relying on land-use classification and carbon pool values, and not accounting for carbon movement between pools. Also, climate-smart practices involve using certain plant species exposed to additional factors like pests, wind, and weather, which can affect their growth differently than in a natural ecosystem. Consequently, it is essential to generate detailed and accurate maps and conduct field sampling to better understand the benefits of climate-smart practices in carbon storage and sequestration.
5. Conclusions
From the financial and economic standpoints, we assessed the feasibility of 10 climate-smart practices compatible with the transition to sustainable agricultural practices under integrated landscape management in three priority watersheds in Mexico. The evaluation included the biophysical modeling of potential co-benefits regarding the carbon sequestration and landscape connectivity of three selected practices, which were projected under BAU and ILM scenarios for two periods (2026 and 2041), using 2022 as the base year. The results showed that most climate-smart practices are viable in the medium and long term from a private standpoint. However, more significant benefits in a shorter period are achievable when social and ecological co-benefits are considered. Also, the projected scenarios showed that live fences, isolated trees in pastures, and riparian vegetation would decelerate or compensate for deforestation trends, which is expected to continue in the three evaluated watersheds.
The effect of the biophysically evaluated climate-smart practices on vegetation dynamics improved the provision of carbon services and landscape connectivity in the medium and long term (2026 and 2041). However, the magnitude of these socioecological benefits depends on the case study and the implementation intensity (conservative or ambitious), ranging from decelerating the loss of carbon sequestration capacity and landscape connectivity in the long term to achieving higher values of both services in the medium-term time horizon (2026) than in the base year (2022).
Despite the practices preventing significant losses in connectivity and carbon services, carbon’s value decreases over time due to the time value of money and deforestation trends. Nonetheless, implementing the practices is more profitable than doing nothing. Biophysical modeling can support cost-benefit analysis, and the results of the CBA and this integrated valuation approach provide a comprehensive understanding of the ESV, supporting budgetary allocations and prioritizing conservation and social benefits.
Integrating biophysical assessments of ecosystem services provision with financial and economic dimensions is crucial to promoting climate change adaptation and gender-focused green recovery initiatives within resource-constrained contexts. A more holistic assessment of the socioeconomic and environmental costs and benefits aids policymakers, municipal institutions, and companies in internalizing those outcomes into informed policy- and decision-making. For example, these evaluations could guide the design of more effective policies for adopting sustainable cattle ranching and agroforestry, enhance municipal planning processes to optimize resource allocation into more resilient and equitable alternatives, and be integrated into business strategies to improve private companies’ long-term sustainability, including environmental risk reduction. Moreover, incorporating traditional knowledge into these strategies can enhance community resilience, emphasizing the importance of multifaceted assessments in fostering sustainable development.
Furthermore, the data generated in this study are a starting point for assessing the feasibility and co-benefits of different climate-smart practices, prioritizing sites for implementation at the watershed scale, and evaluating potential outcomes at the landscape level. However, the results depend on multiple factors such as the species combination, survival rates, climate variability, local preferences, stakeholder involvement, market fluctuations, and policy changes, in which future transdisciplinary research is critical. Additionally, a fine-scale analysis with long-term monitoring, including field data, would improve the understanding of the spatial variations in effectiveness, allowing for adaptive management and guiding future scaling efforts.
Author Contributions
Conceptualization, D.L.; methodology, D.L. and J.J.V.T.; software, J.J.V.T.; validation, J.J.V.T.; formal analysis, J.J.V.T., D.L. and D.A.R.-F.; investigation, J.J.V.T., D.L. and D.A.R.-F.; writing—original draft preparation D.L.; writing—review and editing, J.J.V.T., D.L., D.A.R.-F., M.d.P.S.-V. and A.R.d.l.S.; visualization, J.J.V.T. and D.L.; supervision, M.d.P.S.-V. and A.R.d.l.S.; project administration, M.d.P.S.-V. and A.R.d.l.S. All authors have read and agreed to the published version of the manuscript.
Funding
The presented results were derived from the targeted technical assistance “Economic Valuation of Ecosystem Services to Strengthen Integrated Landscape Management in Selected Watersheds in Mexico”, financed by the World Bank’s Global Program for Sustainability Trust Fund (TF0C0157) and under “Connecting Watershed Health with Sustainable Livestock and Agroforestry Production”, CONECTA project.
Informed Consent Statement
Not applicable.
Data Availability Statement
Data are contained within the article.
Acknowledgments
The authors are grateful to the National Institute of Ecology and Climate Change (INECC) and the World Bank’s Global Program for Sustainability Trust Fund for their support, to Citlalli A. González Hernández for copyediting and proofreading the manuscript, and to the three anonymous reviewers and the editors for their thorough comments and recommendations on the manuscript’s earlier versions.
Conflicts of Interest
The authors declare no conflicts of interest.
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Figure 2.
Amount of carbon storage and sequestration in the three watersheds (A) and zones with a high increase (blue) or loss (red) between 2002 and 2018 (B).
Figure 2.
Amount of carbon storage and sequestration in the three watersheds (A) and zones with a high increase (blue) or loss (red) between 2002 and 2018 (B).
Figure 3.
Landscape connectivity values per study case: Ameca-Mascota, Del Carmen, and Jamapa.
Figure 3.
Landscape connectivity values per study case: Ameca-Mascota, Del Carmen, and Jamapa.
Table 1.
Climate-smart practices assessed and evaluation type.
Table 1.
Climate-smart practices assessed and evaluation type.
Climate-Smart Practices | Biophysical Evaluation | Type of Economic Valuation | Description |
---|---|---|---|
Protein fodder banks | No * | Private, social or both | Refers to planting herbs, trees, or shrubs with high protein content, which can be harvested and taken to the animals in a cut-and-carry system. |
Silvopastoral production | No * | Private, social or both | Livestock production mixing cattle, pasture, trees, and shrubs (woody perennials) in the same area. |
Water distribution systems | No * | Private, social or both | Installing a water delivery system such as a pipeline network to supply the livestock watering troughs on the farms. |
Technical assistance on breeding techniques and reproductive technologies | No * | Private, social or both | Implementing a counselling program for cattle breeding improvement. |
Technical assistance on livestock water-quality monitoring | No * | Private, social or both | Implementing a technical counselling program for water quality monitoring for livestock drinking. |
Traditional subsistence/small-scale farming | No * | Private, social or both | Installing traditional backyard gardens for self-consumption. |
Live fences (multi-strata in Ameca-Mascota and Jamapa) and shrubs (Carmen) | Yes | Private, social or both | Live fences are established surrounding the parcels. The main benefits are help in soil conservation, providing shade, windbreak for the compound, and production of mulch, fruit, bee forage, wood, and habitat. |
Isolated trees in pastures | Yes | Private, social or both | Increasing the number of arboreal elements within the agricultural matrix at key sites would enhance vegetation recovery and habitat for some species. |
Restoration of riparian vegetation (Ameca-Mascota and Jamapa) | Yes | Private, social or both | Active planting to replace any missing riparian vegetation strata. |
Improved grazing management to restore soil carbon sequestration (Del Carmen) | Yes | Private, social or both | Decreasing grazing pressure and providing adequate pasture rest time. |
Table 2.
Adjustment for inflation, weighting formula based on connectivity, weighting formula based on total production value, willingness to pay per municipality, economic value of scenic beauty, and net present value formula.
Table 2.
Adjustment for inflation, weighting formula based on connectivity, weighting formula based on total production value, willingness to pay per municipality, economic value of scenic beauty, and net present value formula.
Formulas | Variables |
---|---|
Formula (1). Adjustment for inflation | |
P2021 = deflated price 2021. Pt = price in year t that must be adjusted. CPIt = Consumer Price Index (energy sector and tariffs) for 2021 and the year t. |
|
Formula (2). Weighting based on connectivity importance formula | |
PdPCi = weighting based on the level of importance of connectivity of municipality i. dPC = level of importance of connectivity for the municipality of Puerto Vallarta (m) and for another municipality (i). |
|
Formula (3). Weighting based on total production value | |
PVPi = weighting based on the production value of the municipality i. VP = production value for Puerto Vallarta (m) and another municipality (i). |
|
Formula (4). Willingness to pay per municipality | |
WTPi = willingness to pay for the scenic beauty of the municipality i. WTPt = willingness to pay for the scenic beauty of the municipality of Puerto Vallarta. PdPCi = weighting based on the level of importance of connectivity of the municipality i. PVPi = weighting based on the production value of the municipality i. P2021 = deflated price for the year 2021. |
|
Formula (5). The economic value of scenic beauty | |
EVc = economic value for the scenic beauty of watershed “c” (c = Ameca-Mascota, del Carmen and Jamapa). WTPi = willingness to pay for the scenic beauty of the municipality i. No. Touristsi = the number of tourists visiting municipality “i” per year. Data were obtained from the Ministry of Tourism (SECTUR) and the official websites of the municipalities. |
|
Formula (6). Net Present Value | |
NPV = Net Present Value Vt = the (private) income or (social) benefits minus the (private or social) costs of the different t periods analyzed. Both private income or benefits and private share costs are included. k = interest rate or the opportunity cost of the investment. This rate makes it possible to transform a flow of money to be realized in the future into a flow of money in the present. A rate of 10% is considered based on the Mexican Ministry of Finance and Public Credit (SHCP) stipulations for the economic analysis of projects. Additionally, scenarios with 6 and 9% rates are considered for the sensitivity analysis. I0 = investment made at the beginning of the first year of implementing the analyzed practice. t = the periods considered for the analyzed practices were 2022 (t = 0), 2026 (t = 4), and 2041 (t = 19). NPV result = NPV > 0 means the practice is feasible for implementation; NPV = 0 means the practice is indifferent to implementation; and NPV < 0 means the practice is not viable for implementation. |
Table 3.
Modeling characteristics of the climate-smart practices.
Table 3.
Modeling characteristics of the climate-smart practices.
Climate-Smart Practice | Definition/Modelling Characteristics |
---|---|
Live fences | 10 m-diameter buffer around selected parcels. 5 m-diameter buffer around selected plots. |
Scattered trees in pastures | |
Other practices | 20 m buffers (10 m on each side) around permanent and intermittent water currents within the priority sites. The total area of pastures was considered to evaluate the effects of improved grazing management to restore soil carbon sequestration capacity. |
Table 4.
Willingness to pay of potential tourists for a landscape with higher level of connectivity.
Table 4.
Willingness to pay of potential tourists for a landscape with higher level of connectivity.
WTP for conservation | Proportion of Tourists | Ameca-Mascota | Del Carmen | Jamapa |
10% | USD 22.7 | USD19.0 | USD 16.5 | |
40% | USD 90.9 | USD 76.1 | USD 66.4 | |
60% | USD 136.4 | USD 114.1 | USD 99.6 | |
80% | USD 181.8 | USD 152.1 | USD 132.8 | |
Opportunity cost of cattle ranching | USD 19.9 | USD 19.1 | USD 12.8 |
Table 5.
The total area of selected practices per watershed under two assessment perspectives.
Table 5.
The total area of selected practices per watershed under two assessment perspectives.
Watershed | Practice | Perspectives | |
---|---|---|---|
Ameca-Mascota | Perspective 1 (47 parcels)/ha | Perspective 2 (47 parcels)/ha | |
Riparian Vegetation | 1.66 | 39.34 | |
Multi-strata live fences | 38.32 | 235.86 | |
Scattered trees in pastures | 3.93 | 3.93 | |
Jamapa | Perspective 1 (47 parcels)/ha | Perspective 2 (47 parcels)/ha | |
Riparian Vegetation | 7.55 | 18.70 | |
Multi-strata live fences | 83.10 | 376.68 | |
Scattered trees in pastures | 3.93 | 3.93 | |
Del Carmen | Perspective 1 (15 parcels)/ha | ||
Live fences with shrubs | 54.35 | ||
Grazing management | 1670.44 |
Table 6.
Carbon sequestration and landscape connectivity under BAU and ILM scenarios for 2022, 2026, and 2041.
Table 6.
Carbon sequestration and landscape connectivity under BAU and ILM scenarios for 2022, 2026, and 2041.
Ameca-Mascota | |||||||
Benefits | 2022 | Scenarios | 2026 | 2041 | |||
Perspective 1 | Perspective 2 | Perspective 1 | Perspective 2 | ||||
Vegetation cover (ha) | 2075.65 | BAU | 1722.79 | 1681.28 | |||
ILM scenario | 1767.82 | 1981.53 | 1726.30 | 1940.01 | |||
Connectivity | 0.07 | BAU | 0.06 | 0.06 | |||
ILM scenario | 0.08 | 0.18 | 0.08 | 0.18 | |||
Carbon (tC/year) | 259,415.16 | BAU | 215,314.16 | 210,125.86 | |||
ILM scenario | 220,941.78 | 247,652.04 | 215,752.61 | 242,462.87 | |||
Jamapa | |||||||
Benefits | 2022 | Scenarios | 2026 | 2041 | |||
Perspective 1 | Perspective 2 | Perspective 1 | Perspective 2 | ||||
Vegetation cover (ha) | 12,025.74 | BAU | 9019.30 | 7215.44 | |||
ILM scenario | 9113.88 | 9418.61 | 7215.44 | 7614.75 | |||
Connectivity | 0.19 | BAU | 0.11 | 0.12 | |||
ILM scenario | 0.12 | 0.15 | 0.17 | 0.19 | |||
Carbon (tC/year) | 2,141,151.04 | BAU | 1,608,863.28 | 1,287,090.62 | |||
ILM scenario | 1,625,734.46 | 1,680,091.75 | 1,287,090.62 | 1,358,319.09 | |||
Del Carmen | |||||||
Benefits | 2022 | Scenarios | 2026 | 2041 | |||
Pasture (ha) | 1670.44 | BAU/ILM scenarios | 1670.44 | ||||
Carbon (tC/year) | 208,772.18 | BAU | 208,772.18 | ||||
ILM scenario | 273,491.56 |
Table 7.
Net present value—private standpoint for each practice and year in the three study cases.
Table 7.
Net present value—private standpoint for each practice and year in the three study cases.
Ameca-Mascota | Jamapa | Del Carmen | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Practice | Unit | NPV 2022 | NPV 2026 | NPV 2041 | NPV 2022 | NPV 2026 | NPV 2041 | NPV 2022 | NPV 2026 | NPV 2041 |
BAU | 1 ha | −$95.3 | −$381.2 | −$1810.9 | $400.9 | $1603.4 | $7616.3 | $21.7 | $86.6 | $411.3 |
Protein fodder banks | 1 ha | −$336.8 | −$133.8 | $261.4 | −$336.9 | −$133.8 | $261.4 | −$336.9 | −$133.8 | $261.4 |
Silvopastoral production | 1 ha | $12.4 | $49.7 | $235.9 | $740.9 | $2963.4 | $14,076.3 | NA | NA | NA |
Water distribution systems (pumping) | 1 m | −$0.8 | −$0.9 | −$1.1 | −$0.8 | −$0.9 | −$1.1 | −$0.8 | −$0.9 | −$1.1 |
Water distribution systems (gravity) | 1 m | −$0.6 | −$0.7 | −$0.9 | −$0.6 | −$0.7 | −$0.9 | −$0.6 | −$0.7 | −$0.9 |
Technical assistance on breeding techniques and reproductive technologies | 1 ha | $455.4 | $2001.8 | $6631.7 | $616.8 | $2711.3 | $8982.4 | $71.7 | $315.1 | $1043.9 |
Technical assistance on livestock water-quality monitoring | Per visit | −$1400.4 | −$3476.2 | −$11,716.6 | −$1400.4 | −$3476.2 | −$11,716.6 | −$1400.4 | −$3476.2 | −$11,716.6 |
Traditional subsistence/small-scale farming | Per farm | −$39.5 | $82.3 | $302.2 | −$39.5 | $82.3 | $302.2 | −$39.5 | $82.3 | $302.3 |
Multi-strata live fences | 1 m | $2.3 | $10.3 | $26.0 | $2.3 | $10.3 | $26.0 | NA | NA | NA |
Isolated trees in pastures | 1 ha | $142.9 | $961.3 | $2565.9 | $142.9 | $961.3 | $2565.9 | NA | NA | NA |
Restoration of riparian vegetation | 1 ha | −$443.9 | −$488.1 | −$579.0 | −$443.9 | −$488.1 | −$579.0 | NA | NA | NA |
Live shrub fences | 1 ha | NA | NA | NA | NA | NA | NA | −$57.5 | $262.4 | $888.9 |
Improved grazing management to restore soil carbon sequestration | 1 ha | NA | NA | NA | NA | NA | NA | $17.9 | $71.5 | $339.5 |
Table 8.
Net present value–social standpoint for each practice and year in the three study cases.
Table 8.
Net present value–social standpoint for each practice and year in the three study cases.
Site | NPV-Year | Carbon (Millions of USD) | Landscape Connectivity | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Social Value | California Market | WTP (Thousands of USD) | Opportunity Cost (Millions of USD) | ||||||||||
BAU | ILM Scenario | BAU | ILM Scenario | BAU | ILM Scenario | BAU | ILM Scenario | ||||||
P1 | P2 | P1 | P2 | P1 | P2 | P1 | P2 | ||||||
Ameca-Mascota | NPV 2022 | $6.8 | NA | NA | $3.9 | NA | NA | $13.0 | NA | NA | $19.9 | NA | NA |
NPV 2026 | $5.6 | $5.8 | $6.5 | $3.2 | $3.3 | $3.7 | $9.5 | $12.4 | $22.3 | $17.1 | $22.7 | $51.1 | |
NPV 2041 | $5.5 | $5.6 | $6.4 | $3.2 | $3.3 | $3.7 | $9.2 | $12.1 | $21.8 | $17.9 | $25.6 | $59.7 | |
Del Carmen | NPV 2022 | $5.5 | NA | $3.2 | NA | NA | NA | NA | $19.1 | NA | NA | ||
NPV 2026 | $5.5 | $7.2 | $3.2 | $4.2 | NA | NA | NA | NA | NA | NA | |||
NPV 2041 | $5.5 | $7.2 | $3.2 | $4.2 | NA | NA | NA | NA | NA | NA | |||
Jamapa | NPV 2022 | $56.5 | NA | NA | $32.8 | NA | NA | $75.4 | NA | NA | $12.8 | NA | NA |
NPV 2026 | $42.4 | $42.8 | $44.2 | $24.6 | $24.8 | $25.7 | $49.8 | $64.1 | $101.5 | $7.4 | $8.1 | $10.1 | |
NPV 2041 | $33.4 | $33.7 | $33.9 | $19.1 | $19.5 | $19.7 | $39.8 | $50.7 | $81.2 | $7.3 | $10.8 | $12.1 |
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