Are indigenous territories effective natural climate solutions? A neotropical analysis using matching methods and geographic discontinuity designs

Indigenous Territories (ITs) with less centralized forest governance than Protected Areas (PAs) may represent cost-effective natural climate solutions to meet the Paris agreement. However, the literature has been limited to examining the effect of ITs on deforestation, despite the influence of anthropogenic degradation. Thus, little is known about the temporal and spatial effect of allocating ITs on carbon stocks dynamics that account for losses from deforestation and degradation. Using Amazon Basin countries and Panama, this study aims to estimate the temporal and spatial effects of ITs and PAs on carbon stocks. To estimate the temporal effects, we use annual carbon density maps, matching analysis, and linear mixed models. Furthermore, we explore the spatial heterogeneity of these estimates through geographic discontinuity designs, allowing us to assess the spatial effect of ITs and PAs boundaries on carbon stocks. The temporal effects highlight that allocating ITs preserves carbon stocks and buffer losses as well as allocating PAs in Panama and Amazon Basin countries. The geographic discontinuity designs reveal that ITs’ boundaries secure more extensive carbon stocks than their surroundings, and this difference tends to increase towards the least accessible areas, suggesting that indigenous land use in neotropical forests may have a temporarily and spatially stable impact on carbon stocks. Our findings imply that ITs in neotropical forests support Nationally Determined Contributions (NDCs) under the Paris Agreement. Thus, Indigenous peoples must become recipients of countries’ results-based payments.

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Are indigenous territories and community-managed areas effective natural climate solutions? A neotropical analysis using matching methods and geographic discontinuity designs

10.1101/2020.12.23.424126 ◽

2020 ◽

Author(s):

Camilo Alejo ◽

Chris Meyer ◽

Wayne S. Walker ◽

Seth R. Gorelik ◽

Carmen Josse ◽

...

Keyword(s):

Amazon Basin ◽

National Level ◽

Spatial Effect ◽

Carbon Stocks ◽

Paris Agreement ◽

Neotropical Forests ◽

Temporal Effects ◽

Indigenous Territories ◽

Temporal And Spatial ◽

Natural Climate

AbstractIndigenous Territories (ITs) and Community Managed Protected Areas (PAs) with less restriction on forest use than integral PAs may represent cost-effective natural climate solutions to meet the Paris agreement. However, the literature has been limited to examining the effect of ITs and Community Managed PAs on deforestation, despite the influence of anthropogenic degradation. Thus, little is known about the temporal and spatial effect of allocating ITs and Community Managed PAs on carbon stocks dynamics that account for losses from deforestation and degradation. Using Amazon Basin countries and Panama at the national level, and Petén (Guatemala) and Acre (Brazil) at the subnational level, this study aims to estimate the temporal and spatial effects of ITs and PAs on carbon stocks. To estimate the temporal effects, we use annual carbon density maps, matching analysis, and linear mixed models. Furthermore, we explore the spatial biases derived from matching analysis and use geographic discontinuity designs to assess the spatial effect of PAs and ITs boundaries on carbon stocks. The temporal effects highlight that allocating ITs preserves carbon stocks and buffer losses as PAs in Panama and Amazon Basin countries. Community Managed PAs temporal effect on carbon stocks surpasses that of integral PAs in Petén (Guatemala) and Acre (Brazil). The geographic discontinuity designs reveal that ITs and Community Managed PAs boundaries secure more extensive carbon stocks than their surroundings, and this difference tends to increase towards the least accessible areas. These results also suggest that indigenous and community land-use in neotropical forests may have a limited and stable spatial impact on carbon stocks. Our findings imply that ITs and Community Managed PAs in neotropical forests support Nationally Determined Contributions (NDCs) under the Paris Agreement. Thus, Indigenous peoples and local communities must become recipients of countries’ results-based payments.

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The role of action effects in motor sequence planning and execution: exploring the influence of temporal and spatial effect anticipation

Psychological Research ◽

10.1007/s00426-021-01525-2 ◽

2021 ◽

Author(s):

Rachel M. Brown ◽

Erik Friedgen ◽

Iring Koch

Keyword(s):

Action Planning ◽

Spatial Effects ◽

Motor Sequence ◽

Action Sequence ◽

Sequential Action ◽

Spatial Features ◽

Temporal Features ◽

Action Sequences ◽

Temporal And Spatial ◽

Anticipation Effect

AbstractActions we perform every day generate perceivable outcomes with both spatial and temporal features. According to the ideomotor principle, we plan our actions by anticipating the outcomes, but this principle does not directly address how sequential movements are influenced by different outcomes. We examined how sequential action planning is influenced by the anticipation of temporal and spatial features of action outcomes. We further explored the influence of action sequence switching. Participants performed cued sequences of button presses that generated visual effects which were either spatially compatible or incompatible with the sequences, and the spatial effects appeared after a short or long delay. The sequence cues switched or repeated across trials, and the predictability of action sequence switches was varied across groups. The results showed a delay-anticipation effect for sequential action, whereby a shorter anticipated delay between action sequences and their outcomes speeded initiation and execution of the cued action sequences. Delay anticipation was increased by predictable action switching, but it was not strongly modified by the spatial compatibility of the action outcomes. The results extend previous demonstrations of delay anticipation to the context of sequential action. The temporal delay between actions and their outcomes appears to be retrieved for sequential planning and influences both the initiation and the execution of actions.

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Natural climate solutions for Canada

Science Advances ◽

10.1126/sciadv.abd6034 ◽

2021 ◽

Vol 7 (23) ◽

pp. eabd6034

Author(s):

C. Ronnie Drever ◽

Susan C. Cook-Patton ◽

Fardausi Akhter ◽

Pascal H. Badiou ◽

Gail L. Chmura ◽

...

Keyword(s):

Cover Crops ◽

Fossil Fuel ◽

Paris Agreement ◽

Soil Productivity ◽

Potential Contribution ◽

Mitigation Potential ◽

Natural Systems ◽

Clean Air ◽

Natural Climate ◽

Fossil Fuel Emissions

Alongside the steep reductions needed in fossil fuel emissions, natural climate solutions (NCS) represent readily deployable options that can contribute to Canada’s goals for emission reductions. We estimate the mitigation potential of 24 NCS related to the protection, management, and restoration of natural systems that can also deliver numerous co-benefits, such as enhanced soil productivity, clean air and water, and biodiversity conservation. NCS can provide up to 78.2 (41.0 to 115.1) Tg CO2e/year (95% CI) of mitigation annually in 2030 and 394.4 (173.2 to 612.4) Tg CO2e cumulatively between 2021 and 2030, with 34% available at ≤CAD 50/Mg CO2e. Avoided conversion of grassland, avoided peatland disturbance, cover crops, and improved forest management offer the largest mitigation opportunities. The mitigation identified here represents an important potential contribution to the Paris Agreement, such that NCS combined with existing mitigation plans could help Canada to meet or exceed its climate goals.

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Variable Selection for the Spatial Autoregressive Model with Autoregressive Disturbances

Mathematics ◽

10.3390/math9121448 ◽

2021 ◽

Vol 9 (12) ◽

pp. 1448

Author(s):

Xuan Liu ◽

Jianbao Chen

Keyword(s):

Variable Selection ◽

Autoregressive Model ◽

Rapid Development ◽

Heterogeneous Data ◽

Spatial Effect ◽

Spatial Effects ◽

Spatial Autoregressive Model ◽

Monte Carlo Studies ◽

Spatial Autoregressive ◽

Theoretical Results

Along with the rapid development of the geographic information system, high-dimensional spatial heterogeneous data has emerged bringing theoretical and computational challenges to statistical modeling and analysis. As a result, effective dimensionality reduction and spatial effect recognition has become very important. This paper focuses on variable selection in the spatial autoregressive model with autoregressive disturbances (SARAR) which contains a more comprehensive spatial effect. The variable selection procedure is presented by using the so-called penalized quasi-likelihood approach. Under suitable regular conditions, we obtain the rate of convergence and the asymptotic normality of the estimators. The theoretical results ensure that the proposed method can effectively identify spatial effects of dependent variables, find spatial heterogeneity in error terms, reduce the dimension, and estimate unknown parameters simultaneously. Based on step-by-step transformation, a feasible iterative algorithm is developed to realize spatial effect identification, variable selection, and parameter estimation. In the setting of finite samples, Monte Carlo studies and real data analysis demonstrate that the proposed penalized method performs well and is consistent with the theoretical results.

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Multiple Evidence for Climate Patterns Influencing Ecosystem Productivity across Spatial Gradients in the Venice Lagoon

Journal of Marine Science and Engineering ◽

10.3390/jmse9040363 ◽

2021 ◽

Vol 9 (4) ◽

pp. 363

Author(s):

Camilla Bertolini ◽

Edouard Royer ◽

Roberto Pastres

Keyword(s):

Time Series ◽

Primary Productivity ◽

Clustering Analysis ◽

Venice Lagoon ◽

Spatial Effects ◽

Spatial Gradients ◽

Climate Forecasting ◽

Oxygen Production Rate ◽

Temporal And Spatial

Effects of climatic changes in transitional ecosystems are often not linear, with some areas likely experiencing faster or more intense responses, which something important to consider in the perspective of climate forecasting. In this study of the Venice lagoon, time series of the past decade were used, and primary productivity was estimated from hourly oxygen data using a published model. Temporal and spatial patterns of water temperature, salinity and productivity time series were identified by applying clustering analysis. Phytoplankton and nutrient data from long-term surveys were correlated to primary productivity model outputs. pmax, the maximum oxygen production rate in a given day, was found to positively correlate with plankton variables measured in surveys. Clustering analysis showed the occurrence of summer heatwaves in 2008, 2013, 2015 and 2018 and three warm prolonged summers (2012, 2017, 2019) coincided with lower summer pmax values. Spatial effects in terms of temperature were found with segregation between confined and open areas, although the patterns varied from year to year. Production and respiration differences showed that the lagoon, despite seasonality, was overall heterotrophic, with internal water bodies having greater values of heterotrophy. Warm, dry years with high salinity had lower degrees of summer autotrophy.

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Study on the Temporal and Spatial Effect of the Relief Pressure of Upper Protective Layer Mining

MATEC Web of Conferences ◽

10.1051/matecconf/201710003038 ◽

2017 ◽

Vol 100 ◽

pp. 03038

Author(s):

Chengcheng Li

Keyword(s):

Protective Layer ◽

Spatial Effect ◽

Temporal And Spatial

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Brazilian Mangroves: Blue Carbon Hotspots of National and Global Relevance to Natural Climate Solutions

Frontiers in Forests and Global Change ◽

10.3389/ffgc.2021.787533 ◽

2022 ◽

Vol 4 ◽

Author(s):

Andre S. Rovai ◽

Robert R. Twilley ◽

Thomas A. Worthington ◽

Pablo Riul

Keyword(s):

Carbon Sequestration ◽

National Level ◽

Cost Effective ◽

Woody Biomass ◽

Carbon Stocks ◽

Blue Carbon ◽

Carbon Accumulation ◽

Amazon Forest ◽

Nationally Determined Contributions ◽

Natural Climate

Mangroves are known for large carbon stocks and high sequestration rates in biomass and soils, making these intertidal wetlands a cost-effective strategy for some nations to compensate for a portion of their carbon dioxide (CO2) emissions. However, few countries have the national-level inventories required to support the inclusion of mangroves into national carbon credit markets. This is the case for Brazil, home of the second largest mangrove area in the world but lacking an integrated mangrove carbon inventory that captures the diversity of coastline types and climatic zones in which mangroves are present. Here we reviewed published datasets to derive the first integrated assessment of carbon stocks, carbon sequestration rates and potential CO2eq emissions across Brazilian mangroves. We found that Brazilian mangroves hold 8.5% of the global mangrove carbon stocks (biomass and soils combined). When compared to other Brazilian vegetated biomes, mangroves store up to 4.3 times more carbon in the top meter of soil and are second in biomass carbon stocks only to the Amazon forest. Moreover, organic carbon sequestration rates in Brazilian mangroves soils are 15–30% higher than recent global estimates; and integrated over the country’s area, they account for 13.5% of the carbon buried in world’s mangroves annually. Carbon sequestration in Brazilian mangroves woody biomass is 10% of carbon accumulation in mangrove woody biomass globally. Our study identifies Brazilian mangroves as a major global blue carbon hotspot and suggest that their loss could potentially release substantial amounts of CO2. This research provides a robust baseline for the consideration of mangroves into strategies to meet Brazil’s intended Nationally Determined Contributions.

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