Land-use Model Research Articles

Investigating biodiversity trends in different mitigation scenarios with a national integrated assessment model

ABSTRACT The inclusion of Sustainable Development Goals (SDGs) in climate mitigation pathways is critical and can be reached by assessing their consequences through the deployment of appropriate indicators to that end. Integrated Assessment Models (IAMs) are important tools for understanding possible impacts caused by adopting new policies. We investigate terrestrial biodiversity trends (life on land: SDG 15) for three different climate mitigation scenarios for Brazil: (1) A scenario compatible with a world that maintains its current policies with current deforestation rates in the Amazon and the Cerrado biomes; (2) A scenario in which Brazil fulfils its Nationally Determined Contribution (NDC); and (3) A scenario compatible with a world that limits warming to 1.5°C. We use the Brazilian Land-Use and Energy System model (BLUES), a national IAM, to show the implications of the transitions involved in the above-mentioned scenarios for the country up to 2050. We conduct a post-processing analysis using consolidated biodiversity indicators to emphasize how different IAM greenhouse gas (GHG) emissions mitigation solutions present distinct positive and negative potential impacts on biodiversity in Brazil. However, our analysis does not consider the impacts associated with climate change, but only the risks imposed by mitigation policies. Our results indicate that biodiversity loss decreases in the scenarios from (1) to (3), implying that stronger climate change mitigation actions could result in smaller biodiversity losses. We conclude that Brazil has the opportunity to align its biodiversity and climate goals through nature-based solutions (NBS), such as forest conservation, restoration, pasture recovery, and the use of crop-pasture and agroforestry systems.

A study on air pollution exposure of “first and last mile” urban commuters under space-behavior dual verification based on big data, land-use regression model and space syntax

Urban traffic-related air pollution (TRAP) exposure is a serious problem during daily commutes. Previous studies assess the TRAP exposure of commuters mainly based on fastest and lowest-dose routes. In addition, the first and last parts of the commutes (“first and last mile”) are often overlooked, wherein the commuters tend to choose active transport (walking, jogging and cycling) and are highly exposed to air pollution. This study uses a space syntax model to simulate the most likely actual commuting routes (behavior-based routes) of “first and last mile” in Changsha, China, and combine it with a land-use regression (LUR) model to assess the TRAP exposure. Furthermore, the air pollution exposure doses of commuters across the behavior-based routes are compared with those across the fastest and lowest-dose routes. The results indicate that only 5.9%–13% of lowest-dose routes do not overlap the fastest routes, and the differences in both average fine particulate matter (PM2.5) and nitrogen dioxide (NO2) exposure are below 1%. However, up to 53.1%–68% of the behavior-based routes do not overlap with the fastest and lowest-dose routes, and the average accumulated exposure of the behavior-based routes exceeds those of the other routes by 7.8%–12.9%. These findings suggest an underestimation of TRAP exposure and the potential benefits of choosing cleaner routes. The results also indicate that commuters living in sub-central areas are linked with relatively larger commuting time costs and higher accumulated exposure doses. This highlights the need to focus on the environmental issues suffered by the commuters in these areas. In addition, our study suggests that behavioral science methods, such as space syntax, can shed new light on air pollution exposure research and develop targeted interventions to address related environmental problems.

A mathematical programming approach for downscaling multi-layered multi-constraint land-use models

Land-use and land-cover change (LULCC) models are important tools for environmental policy planning. LULCC models are frequently constrained to the generation of projections at a specific resolution. However, subsequent studies or models may require finer resolutions. In this work, a downscaling method for LULCC models is proposed that uses a mathematical programming approach to disaggregate the multiple layers of the land-use change projections while respecting a series of constraints. The method is calibrated and validated with MapBiomas data for the years 2000 and 2018 converted for the GLOBIOM-Brazil model, successfully predicting land-use at a finer resolution. Also, as proof of concept, the calibrated model is also applied for GLOBIOM-Brazil projections for 2050. This paper advances the state-of-the-art by proposing and testing a downscaling method using a mathematical programming approach with spatial effects, that operates on multi-layered land-use projections with a range of constraints while allowing flexibility on the number and type of the specific layers and constraints.

Trends in tropical forest loss and the social value of emission reductions

Reducing global forest losses is essential to mitigate climate change and its associated social costs. Multiple market and non-market factors can enhance or reduce forest loss. Here, to understand the role of non-market factors (for example, policies, climate anomalies or conflicts), we can compare observed trends to a reference (expected) scenario that excludes non-market factors. We define an expected scenario by simulating land-use decisions solely driven by market prices, productivities and presumably plausible decision-making. The land-use allocation model considers economic profits and uncertainties as incentives for forest conversion. We compare reference forest losses in Brazil, the Democratic Republic of Congo and Indonesia (2000–2019) with observed forest losses and assign differences from non-market factors. Our results suggest that non-market factors temporarily lead to lower-than-expected forest losses summing to 11.1 million hectares, but also to phases with higher-than-expected forest losses of 11.3 million hectares. Phases with lower-than-expected forest losses occurred earlier than those with higher-than-expected forest losses. The damages avoided by delaying emissions that would otherwise have occurred represent a social value of US$61.6 billion (as of the year 2000). This result shows the economic importance of forest conservation efforts in the tropics, even if reduced forest loss might be temporary and reverse over time.

Destination choice modeling with spatially distributed constraints

Destination choice models are a key component of any transport and land-use model. Applications in agent-based models allow for destination choice on an individual level including personal variables, like trip purpose, or situational variables. Commonly applied methodologies stem from econometrics, discrete choice theory and utility maximization using either revealed or stated preference data. This paper presents a framework to integrate cross-section flows between distinct geographic areas, which can be obtained from cordon surveys or mobile phone data. Proposed optimization methodology—based on extended shadow price theory—accommodates these complementary data sources as spatially distributed constraints, in addition to the destination capacity constraints such as workplaces. The new generic and robust optimization methodology accounts for constraints as observed on cross-section flows and destination capacities while maintaining econometric choice model theory. As a proof of concept, the suggested methodology is successfully applied in a real-case, agent-based application covering the tri-national Basel region with about 2 million residents, and a large set of 2 · 104 distinct destination alternatives. Due to different wage levels in all three countries and other reasons, the region’s cross-border commuter flows are highly asymmetric. Including data on cross-border flows obtained from a cordon survey, the choice model’s mean deviation declines by 20% and more on a cross-section level and even more so on a choice alternative level, compared to calculations ignoring shadow prices. Moreover, multiple scenario calculations show considerable improvements in planning and forecasting applications. The results demonstrate the suitability and relevance of the proposed approach to optimize destination choice models with limited destination capacities in geographical regions usually characterized by travel demand asymmetries.

Biocultural mapping: unpacking the myth of an unsuitable Country in the arid zone, Willandra Lakes Region World Heritage Area, Australia

Defining spaces and places in retrojective Geographical Information Systems (GIS) of land use and occupancy is not an easy task. Research into the areas described as suitable for land use and occupancy need to incorporate multiple perspectives of what makes a land use patch useful or salient ecologically. The effect of the concept of 'Terra Nullius' and European colonisation is deeply apparent in the current GIS models of historical land use and occupancy of Aboriginal communities within arid zones in Australia. Biocultural zones of land use and occupancy zones omit spaces and places of habitation due to European bias of what a suitable ecological or hydrological land use zone should look like. This article employs Exploratory GIS methods to interrogate the data layers within the Willandra Lakes Region World Heritage Area, NSW, Australia. This work conclusively demonstrates that there are ranges of areas and land suitability zones prior to colonisation in the nineteenth century. In turn, these Exploratory GIS models of an active Country comprehensively address the question of why visually salient areas of hydrological and ecological Indigenous land use and occupancy continue to be ignored, destroyed, and damaged by settlements in semi-arid regions. Biocultural GIS mapping unpacks the myth that areas were empty or uninhabited by Aboriginal communities and underlines the need for biocultural GIS mapping tools to understand the habitable spaces and places of the arid zone.

Simulation Analysis of Land-Use Spatial Conflict in a Geopark Based on the GMOP–Markov–PLUS Model: A Case Study of Yimengshan Geopark, China

The foundation for accurately understanding regional land-use structures and pursuing the coordination of human–land relations is the scientific identification and simulation of temporal and spatial evolution patterns of land-use spatial conflict (LUSC). Based on the production–living–ecological space (PLES) perspective, a land-use spatial conflict identification and intensity diagnosis model (LUCSII) was constructed using a landscape ecology index. The methods of geographic information system (GIS), spatial autocorrelation analysis, and mathematical statistics were used to achieve the spatial pattern of LUSC over the last 20 years, and the GMOP–Markov–PLUS model was used to simulate the evolution of LUSC in the future under various scenarios. The results indicated that our established LUCSII could accurately identify potential land-use spatial conflict areas in geoparks. The GMOP–Markov–PLUS model constructed had also scientifically predicted the future land-use patterns under different scenarios, successfully demonstrating the changing process of spatial conflict pattern evolution. The research proposed three different plans for the long-term land use of YG, including ecological protection, economic development, and long-term development perspectives. Finally, the research further emphasized the importance for sustainable development of geoparks. More attention should be paid to the optimal allocation of land-use structure and the coordinated development of human–land relationships.

Sustainable Low-Carbon Layout of Land around Rail Transit Stations Based on Multi-Modal Spatial Data

With the ever-increasing demand for transport in modern cities, emissions from urban transport are rising. The proportion of carbon emissions in exhaust gas accounts for a large share of society’s total carbon emissions and is increasing. Therefore, urban transport has a sustainable responsibility to reduce carbon emissions. Investigating the factors that influence carbon emissions from transport has become an important practical issue that needs to be addressed. This paper adopts a “bottom-up” theoretical calculation method of transport carbon emissions and establishes the basic distribution model of inter-modal land use around rail transit stations. It clarifies the connection mode of rail transit stations and establishes the distribution model of carbon emission of stations under different building distribution modes, suggesting the planning of building distribution patterns around rail transit stations. This paper proposes a new method to analyze the influencing factors of carbon emissions at rail transit stations based on multi-modal spatial data in order to make full use of the dense characteristics of rail transit stations and reduce carbon emissions.

Association of long-term exposure to ambient air pollution with retinal neurodegeneration: the prospective Alienor study

Chronic exposure to air pollution may have adverse effects on neurodegenerative diseases. Glaucoma, the second leading cause of blindness worldwide, is a neurodegenerative disease of the optic nerve, characterized by progressive thinning of the retinal nerve fiber layer (RNFL). We investigated the relationship of air pollution exposure with longitudinal changes of RNFL thickness in the Alienor study, a population-based cohort of residents of Bordeaux, France, aged 75 years or more. Peripapillary RNFL thickness was measured using optical coherence tomography imaging every 2 years from 2009 to 2020. Measurements were acquired and reviewed by specially trained technicians to control quality. Air pollution exposure (particulate matter ≤2.5 μm (PM2.5), black carbon (BC), nitrogen dioxide (NO2)) was estimated at the participants’ geocoded residential address using land-use regression models. For each pollutant, the 10-year average of past exposure at first RNFL thickness measurement was estimated. Associations of air pollution exposure with RNFL thickness longitudinal changes were assessed using linear mixed models adjusted for potential confounders, allowing for intra-eye and intra-individual correlation (repeated measurements).The study included 683 participants with at least one RNFL thickness measurement (62% female, mean age 82 years). The average RNFL was 90 μm (SD:14.4) at baseline. Exposure to higher levels of PM2.5 and BC in the previous 10 years was significantly associated with a faster RNFL thinning during the 11-year follow-up (−0.28 μm/year (95% confidence interval (CI) [-0.44;-0.13]) and −0.26 μm/year (95% CI [-0.40;-0.12]) per interquartile range increment; p < 0.001 for both). The size of the effect was similar to one year of age in the fitted model (−0.36 μm/year). No statistically significant associations were found with NO2 in the main models.This study evidenced a strong association of chronic exposure to fine particulate matter with retinal neurodegeneration, at air pollution levels below the current recommended thresholds in Europe.

Modeling of land use and cover changes (LUCC) in Deli Serdang Regency, North Sumatra Province

Land use/cover (LUC) is a substantial factor in land management and can influence policy in an area. LUC has the potential to change due to physical, economic, and social aspects. This study aims to analyze the spatialland use and cover changes (LUCC) in Deli Serdang Regency for the 2010 to 2020 period and predict LUC in 2030. The analysis was run by applying the Cellular Automata-Markov Chain method. The driving factors used in this modeling are the distance to the road, the distance to the river, population density, the distance to the district capital, and the distance to Medan city. The results showed that Kappa for image classification was 0.86. The dominant type of LUC in Deli Serdang Regency is a plantation, with a total area of more than 45%, followed by paddy fields, dryland agriculture, forests, and settlements/built-up areas. LUCC model validation obtained a kappa value of 0.89 (very good category) and can be applicated for predicting land use change models in 2030. By 2030, the settlements/built-up area and dryland agriculture will increase significantly, which 21,060 ha and 4,587 ha, respectively, while forests, plantations, and paddy fields will decrease significantly by around 9,266 ha, respectively, respectively 8,306 ha and 7,806 ha.

Assessing the effectiveness of protected areas for panda conservation under future climate and land use change scenarios

While the relatively stable land use and land cover (LULC) patterns is an important feature of protected areas (PAs), the influence of this feature on future species distribution and the effectiveness of the PAs has rarely been explored. Here, we assessed the role of land use patterns within PAs on the projected range of the giant panda (Ailuropoda melanoleuca) by comparing projections inside and outside of PAs for four model configurations: (1) only climate covariates, (2) climate and dynamic land use covariates, (3) climate and static land use covariates and (4) climate and hybrid dynamic-static land use covariates. Our objectives were twofold: to understand the role of protected status on projected panda habitat suitability and evaluate the relative efficacy of different climate modeling approaches. The climate and land use change scenarios used in the models include two shared socio-economic pathways (SSPs) scenarios: SSP126 [an optimistic scenario] and SSP585 [a pessimistic scenario]. We found that models including land-use covariates performed significantly better than climate-only models and that these projected more suitable habitat than climate-only models. Static land-use models projected more suitable habitat than both the dynamic and hybrid models under SSP126, while these models did not differ under SSP585. China's panda reserve system was projected to effectively maintain suitable habitat inside PAs. Panda dispersal ability also significantly impacted outcomes, with most models assuming unlimited dispersal forecasting range expansion and models assuming zero dispersal consistently forecasting range contraction. Our findings highlight that policies targeting improved land-use practices should be an effective means for offsetting some of the negative effects of climate change on pandas. As the effectiveness of PAs is projected to be maintained, we recommend the judicious management and expansion of the PA system to ensure the resilience of panda populations into the future.

An Improved Future Land-Use Simulation Model with Dynamically Nested Ecological Spatial Constraints

A land-use simulation model oriented toward ecological constraints is effective for evaluating the ecological impact of urban spatial planning. However, few studies have incorporated dynamically nested ecological spatial constraints into the model or fully considered the urban development, agricultural production, and ecological function among the ecological spatial constraints. Therefore, this study developed an improved land-use simulation model with dynamically nested ecological spatial constraints (LSDNE). We fully considered the multilevel ecological spatial constraints from the perspectives of ecological (ecological protection red line, EPRL), production (capital farmland, CF), and living (urban development land-use suitability, UDLS). Five scenarios in terms of future land-use distribution in 2030 were set, namely, inertial development (S1), considering EPRL (S2), considering CF (S3), considering EPRL and CF (S4), and considering EPRL, CF, and UDLS (S5). This new approach was implemented in the rapidly developing provincial capital city of Changchun, China. The results show that, due to the occupation of arable land, Changchun had the largest increase in built-up land (2019.75 km2 to 3036.36 km2) from 2010 to 2020. Terrain elevation was the most significant factor in all kinds of land expansion. According to future land spatial distribution results in 2030, under S4, Changchun’s built-up land will be more compact compared with S1–S3 and S5, which showed more scattered built-up land. These predicted results show that Changchun’s spatial planning put forward high requirements for the efficient use of land and constraints in red-line areas. Due to a clear evaluation of the impact of ecological spatial constraints on future land expansion, the LSDNE model provides more accurate support for the efficient use of land resources and future territorial spatial planning.

Associations of time-weighted individual exposure to ambient particulate matter with carotid atherosclerosis in Beijing, China

BackgroundTime-location information (time spent on commuting, indoors and outdoors around residential and work places and physical activity) and infiltrated outdoor pollution was less considered estimating individual exposure to ambient air pollution. Studies investigating the association between individual exposure to particulate matter (PM) with aerodynamic diameter < 10 μm (PM10) and < 2.5 μm (PM2.5) and carotid atherosclerosis presented inconsistent results. Moreover, combined effect of pollutants on carotid atherosclerosis was not fully explored. We aimed to investigate the association between long-term individual time-weighted average exposure to PM2.5 and PM10 and the risk of carotid atherosclerosis, and further explore the overall effect of co-exposure to pollutants on carotid atherosclerosis.MethodsThe study population included 3069 participants derived from the Beijing Health Management Cohort (BHMC) study. Daily concentration of ambient air pollutants was estimated by land-use regression model at both residential and work addresses, and one- and two-year time-weighted average individual exposure was calculated by further considering personal activity pattern and infiltration of ambient air pollution indoors. We explored the association of PM2.5 and PM10 with carotid atherosclerosis and pooled the overall effect of co-exposure to ambient air pollutants by quantile g-computation.ResultsA significant association between time-weighted average exposure to PM2.5 and PM10 and carotid atherosclerosis was observed. Per interquartile range increase in two-year exposure to PM2.5 (Hazard ratio (HR): 1.322, 95% confidence interval (CI): 1.219–1.434) and PM10 (HR:1.213, 95% CI: 1.116–1.319) showed the strongest association with carotid atherosclerosis, respectively. Individuals in higher quartiles of pollutants were at higher risk for carotid atherosclerosis compared with those in the lowest quartile group. Concentration response functions documented the nearly linear and nonlinear relationship and interpreted the upward trends of the risk for carotid atherosclerosis with increasing level of pollutant concentrations. Moreover, effect estimates for the mixture of pollutants and carotid atherosclerosis were larger than any of the individual pollutants (HR (95% CI) was 1.510 (1.338–1.704) and 1.613 (1.428–1.822) per quartile increase for one-year and two-year time-weighted average exposure, respectively).ConclusionsIndividual time-weighted average exposure to PM2.5 and PM10 was associated with carotid atherosclerosis. Co-exposure to ambient air pollution was also positively associated with carotid atherosclerosis.

Area-based scenario development in land-use change modeling: A system dynamics-assisted approach for mixed agricultural-residential landscapes

This study aimed to enhance land use and land cover (LULC) change models by addressing their main limitations, which include the lack of accountability and temporal stability of driving forces. Additionally, the study aimed to create area-based scenarios to forecast future LULCs, rather than solely relying on distribution-based scenarios. To accomplish this goal, the study developed a coupled System Dynamics (SD) and Cellular Automata (CA) modeling system to simulate possible LULC changes in the Gavkhooni Basin, central Iran. The study utilized LULC maps from Landsat images in 2001, 2011, and 2021 to analyze spatio-temporal land use changes in the region. Agricultural and residential transition suitability layers were produced using a spatial Multi-Criteria Evaluation procedure and applied to inform the CA model in the proper allocation of LULC changes. Three interconnected water supply, agricultural, and residential area projection subsystems were developed using system dynamics method to determine land requirements for LULC conversions from 2020 to 2041, taking into account factors such as water availability, land suitability, agricultural labor force, and economic development. Ten scenarios were developed based on changes in the key variables affecting the limiting factors, such as climatic conditions and water management policies, to project agricultural and residential areas in the future. The CA's spatial allocation informed by transition suitability layers was found to be satisfactory with a Kappa-location value of 0.85. The subsystems were competent in projecting water supply with Mean Absolute Error (MAE) values of 6.57% and the dynamics of agricultural and residential areas with MAE values of 2.94%, whereas those of the Markovian Chain model were found to be 23.02% and 7.5% for agricultural and residential areas, respectively. The study found that available agricultural areas varied significantly between 86.53 and 1480 sq.km under different climatic conditions, irrigation efficiency, and agricultural water assignment coefficients between 2024 and 2033. Residential area demand was found to be increasing with different rates under the scenarios between 47.40 and 73.01 sq.km. The SD-CA coupled framework presented in this research can be viewed as a decision support system to develop compensatory strategies for better management and planning of agricultural and residential lands.

Optimal Modeling of Sustainable Land Use Planning under Uncertain at a Watershed Level: Interval Stochastic Fuzzy Linear Programming with Chance Constraints

In this paper, an uncertain interval stochastic fuzzy chance constraint land use optimal allocation method is proposed and applied to solve the problem of land use planning in river basins. The UISFCL-LUP method is an aggregation of interval parametric programming, fuzzy linear programming and chance constraint programming which can cope with uncertain problems such as interval value, fuzzy set and probability. In this paper, the uncertain mathematical method is explored and studied in the optimal allocation of land use in the next two planning periods of Nansihu Lake Basin in China. Moreover, it was proved that ISFCL-LUP can deal with the uncertainty of interval, membership function and probability representation and can also be used to solve the land use planning and land use strategy analysis under uncertain conditions. On the basis of model calculations, we obtained the optimal allocation results for six types of land use in four regions over two planning periods based on different environmental constraints. The results show that the optimized λ value (that is, the degree of satisfaction with all the model conditions) is in the range of [0.54, 0.79] and the corresponding system benefits are between [18.4, 20.4] × 1012 RMB and [96.7, 109.3] × 1012 RMB. The results indicate that land managers can make judgments based on the different socio-economic development needs of different regions and determine strategic land use allocation plans under uncertain conditions. At the same time, the model obtained interval solutions under different system satisfaction and constraint violation probabilities, which helps land managers to analyze the importance of land system optimization and sustainable development more deeply.

An application of land use, transport, and economy interaction model

Governments need to assess the benefits of transport projects to prioritize investments. It is imperative for governments to have tools that can closely estimate the actual benefits and impacts of investments in significant transport projects.Traditionally, the benefits of a transport project have been estimated by using a transport model, assuming fixed land use for the base and project case in the future. However, this approach cannot measure the impact of land-use changes as residents and businesses relocate to take advantage of lower travel and/or freight costs resulting from the project. Consequently, the benefits of the project may be under or overestimated, depending on its position within the transport network and how it reshapes land use patterns in the future. To overcome this drawback in existing models, this paper firstly presents a literature review of advances in the land use transport interaction model (LUTI), then discusses the development of a LUTI model for Victoria, Australia (VLUTI) by integrating the Victorian Integrated Transport Model with a Spatial Computable General Equilibrium model (SCGE). The land use component (SCGE) was developed to represent a highly resolved zone system, detailed classifications of occupations and industries, and spatial interactions via commuting, and trade in goods and services, and thus enables VLUTI to simulate a detailed and nuanced picture of land use and transport interactions in a complete economic environment.The literature review also indicates limited research in applying a LUTI to estimate land use change benefits. This paper presents an application of VLUTI by looking at differences in the conventional transport benefits under both static and dynamic land use scenarios. In the static method, the land use in the project case is unchanged from the base case. In the dynamic case, the land use in the project case, at a future point in time, is endogenously adjusted within VLUTI. It presents a method to correct, in the dynamic case, the benefits as estimated by the rule of a half, which usually assumes static land use. The paper concludes with suggestions for further developments and improvements of VLUTI such as incorporating an activity-based transport model and an environmental model to progress toward an integrated land-use, transport, economy, and environment model capable of assessing the full impact of a transport project in a holistic way.

What are the main environmental impacts and products contributing to the biodiversity footprint of EU consumption? A comparison of life cycle impact assessment methods and models

PurposeRecent developments in life cycle impact assessment (LCIA) target at better addressing biodiversity impacts, including the extended modeling of drivers of biodiversity loss. This led to the development of multiple LCIA methods addressing the area of protection of ecosystem quality (i.e, biodiversity loss) over time. This paper aims at systematically comparing available operational LCIA methods and models for assessing the main drivers of biodiversity impacts of EU consumption and unveiling similarities and differences among current methods.MethodsThis paper compares the biodiversity impacts of EU consumption by implementing eight LCIA methods and models: five full LCIA methods (namely, LC-IMPACT, Impact World + , Ecological Scarcity 2013, ReCiPe 2016, and Stepwise), a land-use intensity-specific LCIA model, and two approaches based on the GLOBIO model. The EU Consumption Footprint model is adopted as case study. The comparative analysis between the assessed methods aimed at identifying convergent and divergent results regarding the drivers of biodiversity impacts of EU consumption. The analysis focused on four different levels: impact category, representative product (modeled consumed products), inventory process, and elementary flow. The agreement among the methods in defining an element as relevant was evaluated. Finally, gaps among methods were assessed in terms of coverage of impact categories and elementary flows.Results and discussionThe analysis unveiled that there is a certain level of agreement among available LCIA methods and models regarding the most contributing impact categories and products to the overall biodiversity footprint due to EU consumption. Land use, climate change, and ecotoxicity had a major contribution to overall impacts, thereby highlighting their role as drivers of biodiversity loss. Biodiversity impacts were due to a limited number of consumed products, where food (meat), mobility, and household goods were identified as top contributors. Most contributing inventory processes and elementary flows were associated to most contributing representative products (e.g, animal feed). The relevance and presence of elementary flows in LCIA methods and models were heterogeneous for most of the impact categories.ConclusionsThe results of this study highlight the importance of impact category coverage in the assessment of biodiversity impacts. Limited coverage of impact categories (e.g, methods limited to assess land use) might underestimate the impacts of other drivers of biodiversity loss, especially climate change and ecotoxicity. Further efforts are required to assess the effects of spatial regionalization and the inclusion of missing drivers, recently developed in LCIA.

Current policies are insufficient to protect or restore Brazil’s cost-effective conservation priority zones

In Brazil, conservation priority zones, in spite of their key role in preserving natural vegetation and its environmental resources are frequently located outside the country’s public network of protected areas (PAs). Here we present the first study on land-use impacts inside Brazil’s unprotected (i.e. outside PAs) Cost-Effective conservation priority Zones (CEZs), for the period 2020–2050. CEZs are conservation priority zones that had experienced low levels of human impact in 2020. In this study, we consider various governance scenarios, including different deforestation control and native vegetation restoration policies. To this end, a land-use change model is combined with a downscaling method to generate natural vegetation cover projections at a 0.01 resolution. Results, which include the effects of climate change on the expansion of the Brazilian agriculture, project native vegetation losses (through deforestation) or gains (through restoration) inside unprotected CEZs. If the current pattern of disregard for the environment persists, our results indicate that a large share of the native vegetation inside Brazil’s CEZs is likely to disappear, with negative impacts on biodiversity preservation, green-house gas emissions and ecosystem services in general. Moreover, even if fully implemented and enforced, Brazil’s current Forest Code is insufficient to adequately protect CEZs from anthropization, especially in the Cerrado biome. We expect that this study can help improving the conservation and restoration of CEZs in Brazil.

Environmental History and Commons for the Colombian Caribbean Challenges

Environmental history, as a field of analysis and transdisciplinary study, aims to explore the interactions between society and ecosystems from a deep understanding of how relationships between human groups and natural systems change over defined periods of time. This article seeks to delve into the environmental history of the savannahs of the Cesar department in Colombia, documenting the milestones in the privatization of the commons and the degradation of social–ecological systems. Methodologically, satellite images and aerial photographs are analyzed to determine the changes in land cover that reveal the state of the ecosystems, and an ethnographic approach is used to document the perception and valuation of local Afro-descendant communities in the region. The article shows how, over the last 60 years, land-use planning models have favored individual appropriation practices and agroindustrial models over collective forms of pastoralism and small-scale agrifood systems. It also documents the main socio-ecological impacts and the conflicts implicit in different tenure models that should be taken into account in the various public policies related to sustainability, peace building, and the recognition of territorial rights in the Colombian Caribbean.

Modelling of land use and land cover changes and prediction using CA-Markov and Random Forest

We used the Cellular Automata Markov (CA-Markov) integrated technique to study land use and land cover (LULC) changes in the Cholistan and Thal deserts in Punjab, Pakistan. We plotted the distribution of the LULC throughout the desert terrain for the years 1990, 2006 and 2022. The Random Forest methodology was utilized to classify the data obtained from Landsat 5 (TM), Landsat 7 (ETM+) and Landsat 8 (OLI/TIRS), as well as ancillary data. The LULC maps generated using this method have an overall accuracy of more than 87%. CA-Markov was utilized to forecast changes in land usage in 2022, and changes were projected for 2038 by extending the patterns seen in 2022. A CA-Markov-Chain was developed for simulating long-term landscape changes at 16-year time steps from 2022 to 2038. Analysis of urban sprawl was carried out by using the Random Forest (RF). Through the CA-Markov Chain analysis, we can expect that high density and low-density residential areas will grow from 8.12 to 12.26 km2 and from 18.10 to 28.45 km2 in 2022 and 2038, as inferred from the changes occurred from 1990 to 2022. The LULC projected for 2038 showed that there would be increased urbanization of the terrain, with probable development in the croplands westward and northward, as well as growth in residential centers. The findings can potentially assist management operations geared towards the conservation of wildlife and the eco-system in the region. This study can also be a reference for other studies that try to project changes in arid are as undergoing land-use changes comparable to those in this study.