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In Thailand, Napier grass is expected to play an important role as an energy resource for future power generation. To accomplish this goal, numerous areas are required for Napier grass plantations. Before introducing crops, the land potential of the country and the impact of crops on the environment should be assessed. The soil and water assessment tool (SWAT) model is very useful in investigating crop impacts and land potential. Unfortunately, the crop growth parameters of Napier grass are yet to be identified and, thus, conducting effective analysis has not been possible. Accordingly, in this study, parameter calibration and SWAT model validation of Napier grass production in Thailand was carried out using datasets from eight sites with 93 samples. Parameter sensitivity analysis was performed prior to parameter calibration, the results of which suggest that the radiation use efficiency and potential harvested index are both highly sensitive. The crop growth parameters were calibrated in order of their sensitivity index ranking, and the final values were obtained by reducing the root mean square error from 10.77 to 1.38 t·ha−1. The validation provides satisfactory results with coefficient of determination of 0.951 and a mean error of 0.321 t·ha−1. Using the developed model and calibrated parameters, local Napier grass dry matter yield can be evaluated accurately. The results reveal that, if only abandoned area in Thailand is used, then Napier grass can provide roughly 33,600–44,900 GWh of annual electricity, and power plant carbon dioxide (CO2) emissions can be reduced by approximately 21.2–28.3 Mt-CO2. The spatial distribution of estimated yield obtained in this work can be further utilized for land suitability analysis to help identify locations for Napier grass plantations, anaerobic digesters, and biogas power plants.

Land-use change is a crucial driver for achieving a sustainable future. However, the uncertainties of socioeconomic development could lead to different changes in the future land-use patterns. Using a spatial downscaling framework, this study aims to explore possible land-use patterns that can help achieve sustainable development in the Guangdong–Hong Kong–Macao Greater Bay Area, China (the Greater Bay Area). The framework combines the global Shared Socioeconomic Pathways (SSPs) scenarios with local land planning policies to model land-use changes. First, the Land Change Modeler was used to analyze the land-use changes from 2000 to 2010 and build transition potential submodels each of which demonstrates transition potential of different land-use classes. Second, future projections were made for the “business-as-usual” scenario and five localized SSP scenarios that were downscaled from global scenarios and modified based on the local land planning policy. Hong Kong was considered a typical case in the Greater Bay Area that could be used to demonstrate the application of the projected land-use maps by comparing the biocapacity and ecological footprint and estimating the carbon emissions associated with land use. The results of the future projections of land use made under six future scenarios indicated that there is a significant expansion in the urban area under all the scenarios, with varying degrees of decrease in cropland and forest among the different scenarios. Moreover, a land-use change also led to the change in local biocapacity and carbon emissions. Our analysis indicated that in achieving sustainable development not only urban area and cropland should be involved for consideration but should also cover the balance between all land-use classes, and three policy implications were proposed based on our findings.

The frequent occurrence of wildfires presents a serious threat to human livelihoods and local ecosystems. The use of machine learning (ML) methods to assess wildfire susceptibility can provide decision support for disaster prevention. However, most current ML‐based wildfire susceptibility assessments overly focus on spatially evaluating the disaster threat, while ignoring the potential threats of wildfires to local ecosystems. This situation makes it difficult to determine seasonal variations in wildfire susceptibility and limits the value of assessment results. We present a framework to assess wildfire susceptibility and wildfire threats seasonally to local ecosystems. The ecosystem service value (ESV) was used as a proxy for the economic value of an ecosystem, the random forest algorithm was used to evaluate wildfire susceptibility, and the Daxinganling region, the largest forested area in China, was selected as the study area, and the dynamic equivalent coefficient factor method was used to calculate the ESV of each cell. Our main findings were as follows: (a) wildfire susceptibility exhibited obvious disparities in terms of spatial distribution across the four seasons; (b) each ecosystem in the study area faced a different magnitude of wildfire disturbance; and (c) the expected ESV loss (USD 10.8 billion) due to wildfires was much higher than the region’s total GDP (USD 2 billion) in 2019. This study was repeatable, and all data required were obtained freely. The methodologies used can be applied directly to other regions. Our study will be of particular interest to developing counties where intensive wildfire monitoring is limited. Plain Language Summary The occurrence of wildfires presents a considerable threat to human livelihoods and ecosystems. Spatially assessing wildfire susceptibility will enable the identification of potential wildfire‐related hot spots, and can provide decision support for wildfire management and ecosystem protection. The use of machine learning (ML) methods to evaluate wildfire susceptibility is an effective way to achieve this. However, most of the current ML‐based wildfire susceptibility assessments merely focus on spatial susceptibility assessments and never consider wildfire consequences, making the seasonal variations of wildfire susceptibility difficult to determine and limiting, therefore, the value of the assessment results. We propose a framework that seasonally evaluates wildfire susceptibility and quantifies the economic loss to local ecosystems due to wildfires. The results demonstrated that wildfire susceptibility varied across the four seasons, and the potential loss due to wildfires in the study region was much higher than the region’s GDP in 2019. This study provides a link between wildfire susceptibility assessments and ecosystem service valuation. Key Points A framework was established to seasonally evaluate the wildfire susceptibility based on machine learning Seasonally economic values of ecosystems were computed Wildfire disturbances to each ecosystems across the four seasons were estimated

Napier grass is an energy crop that is promising for future power generation. Since Napier grass has never been planted extensively, it is important to understand the impacts of Napier grass plantations on local energetic, environmental, and socioeconomic features. In this study, the soil and water assessment tool (SWAT) model was employed to investigate the impacts of Napier grass plantation on runoff, sediment, and nitrate loads in Songkhla Lake Basin (SLB), southern Thailand. Historical data, collected between 2009 and 2018 from the U-tapao gaging station located in SLB were used to calibrate and validate the model in terms of precipitation, streamflow, and sediment. The simulated precipitation, streamflow, and sediment showed agreement with observed data, with the coefficients of determination being 0.791, 0.900, and 0.997, respectively. Subsequently, the SWAT model was applied to evaluate the impact of land use change from the baseline case to Napier grass plantation cases in abandoned areas with four different nitrogen fertilizer application levels. The results revealed that planting Napier grass decreased the average surface runoff and sediment in the watershed. A multidisciplinary assessment supporting future decision making was conducted using the results obtained from the SWAT model; these showed that Napier grass will provide enhanced benefits to hydrology and water quality when nitrogen fertilizers of 0 and 125 kgN ha−1 were applied. On the other hand, the benefits to the energy supply, farmer’s income, and CO2 reduction were highest when a nitrogen fertilization of 500 kgN ha−1 was applied. Nonetheless, planting Napier grass should be supported since it increases the energy supply and creates jobs while also reducing surface runoff, sediment yield, nitrate load, and CO2 emission.

This paper analyzed the livelihood vulnerability of households in two communes using socio-economic data, where one site is a climate analogue of the other under expected future climate change. The analysis was undertaken in order to understand local variability in the vulnerability of communities and how it can be addressed so as to foster progress towards rural adaptation planning. The study identified sources of household livelihood vulnerability by exploring human and social capitals, thus linking the human subsystem with existing biophysical vulnerability studies. Selected relevant variables were used in Factor Analysis on Mixed Data (FAMD), where the first eight dimensions of FAMD contributed most variability to the data. Clustering was done based on the eight dimensions, yielding five clusters with a mix of households from the two communes. Results showed that Cluster 3 was least vulnerable due to a greater proportion of households having adopted farming practices that enhance food and water availability. Households in the other clusters will need to make appropriate changes to reduce their vulnerability. Findings show that when analyzing rural vulnerability, rather than broadly looking at spatial climatic and farm management differences, social factors should also be investigated, as they can exert significant policy implications.

To promote Bio-Energy with Carbon dioxide Capture and Storage (BECCS), which aims to replace fossil fuels with bio energy and store carbon underground, and Reducing Emissions from Deforestation and forest Degradation (REDD+), which aims to reduce the carbon emissions produced by forest degradation, it is important to build forest management plans based on the scientific prediction of forest dynamics. For Measurement, Reporting and Verification (MRV) at an individual tree level, it is expected that techniques will be developed to support forest management via the effective monitoring of changes to individual trees. In this study, an end-to-end process was developed: (1) detecting individual trees from Unmanned Aerial Vehicle (UAV) derived digital images; (2) estimating the stand structure from crown images; (3) visualizing future carbon dynamics using a forest ecosystem process model. This process could detect 93.4% of individual trees, successfully classified two species using Convolutional Neural Network (CNN) with 83.6% accuracy and evaluated future ecosystem carbon dynamics and the source-sink balance using individual based model FORMIND. Further ideas for improving the sub-process of the end to end process were discussed. This process is expected to contribute to activities concerned with carbon management such as designing smart utilization for biomass resources and projecting scenarios for the sustainable use of ecosystem services.

Climate and land-use changes are the main drivers of species distribution. On the basis of current and future climate and socioeconomic scenarios, species range projections were made for nine species in the Fabaceae family. Modeled species have instrumental and relational values termed as nature’s contribution to people (NCP). For each species, five scenarios were analyzed resulting in 45 species range maps. Representative concentration pathway (RCP) 4.5 and three shared socioeconomic pathways (SSPs 1, 2, and 3) were used in the analysis. Species ranges under these scenarios were modeled using MaxEnt; a niche modeling software that relates species occurrence with environmental variables. Results were used to compute species richness and evenness based on Shannon’s diversity Index. Results revealed a mix of range expansion and contraction for the modeled species. The findings highlighted which species may remain competitive in an urbanized future and which ones are detrimentally affected by climate. Parts of the country where species abundances are likely to change due to climate and socioeconomic changes were identified. Management of species will be required in people-dominated landscapes to maintain interactions between nature and society, while avoiding natural resource degradation and loss of NCP.

As a global financial center and one of the world’s first-tier cities, Hong Kong is committed to sustainable development and it expects to become the most sustainable city in Asia. With this in mind, this paper evaluates the level of sustainable development in Hong Kong considering the factors of ecological footprint, biocapacity, and the human development index (HDI) from 1995 to 2016, in order to make policy recommendations for transforming Hong Kong into a more sustainable city. Between 1995 and 2016, a period during which the HDI rose, the per capita ecological footprint of Hong Kong increased from 4.842 gha to 6.223 gha. Moreover, fossil energy consumption had a crucial impact on the city’s ecological footprint, whereas the biocapacity of Hong Kong declined gradually. By contrast, Singapore, a city-state with an area similar to Hong Kong’s, presented the opposite situation—the HDI increased while the ecological footprint decreased. We performed a further comparative analysis and a SWOT analysis of Singapore and Hong Kong to elaborate on how to decouple the large ecological footprint from human society development. Concluding that the focus must be on energy consumption, reduction of the human activities’ negative impacts on marine environment, citizens and government, we provide policy suggestions for transforming toward a “high HDI and low footprint” sustainable development society in Hong Kong.

Afforestation projects for mitigating CO2 emissions require to monitor the carbon fixation and plant growth as key indicators. We proposed a monitoring method for predicting carbon fixation in afforestation projects, combining a process‐based ecosystem model and field data and addressed the uncertainty of predicted carbon fixation and ecophysiological characteristics with plant growth. Carbon pools were simulated using the Biome‐BGC model tuned by parameter optimization using measured carbon density of biomass pools on an 11‐year‐old Eucommia ulmoides plantation on Loess Plateau, China. The allocation parameters fine root carbon to leaf carbon (FRC:LC) and stem carbon to leaf carbon (SC:LC), along with specific leaf area (SLA) and maximum stomatal conductance (gsmax) strongly affected aboveground woody (AC) and leaf carbon (LC) density in sensitivity analysis and were selected as adjusting parameters. We assessed the uncertainty of carbon fixation and plant growth predictions by modeling three growth phases with corresponding parameters: (i) before afforestation using default parameters, (ii) early monitoring using parameters optimized with data from years 1 to 5, and (iii) updated monitoring at year 11 using parameters optimized with 11‐year data. The predicted carbon fixation and optimized parameters differed in the three phases. Overall, 30‐year average carbon fixation rate in plantation (AC, LC, belowground woody parts and soil pools) was ranged 0.14–0.35 kg‐C m−2 y−1 in simulations using parameters of phases (i)–(iii). Updating parameters by periodic field surveys reduced the uncertainty and revealed changes in ecophysiological characteristics with plant growth. This monitoring method should support management of afforestation projects by carbon fixation estimation adapting to observation gap, noncommon species and variable growing conditions such as climate change, land use change. Afforestation projects for mitigating CO2 emissions require reliable monitoring of carbon storage and plant growth as key indicators of carbon fixation by afforestation. We developed the monitoring method for predicting carbon fixation in plantations, combining a process‐based ecosystem model and field data. We demonstrated how to estimate plant carbon pools and understand the parameter changes with plant growth stages and the uncertainty of predicted carbon fixation in a plantation by an optimization scheme using field survey data.

People in rural areas often grow foods in their home gardens and share them through food-sharing networks. Besides the obvious economic benefits, such shared food via non-market transactions enriches the inhabitants’ lives by strengthening their social relationships and nutritional quality. These shadow benefits of non-market food are qualitatively recognized, but have not been fully integrated into formal accounting systems. Thus, the present study quantifies the shadow benefits of food-sharing networks by considering the non-market food distribution on Hachijo Island, Japan. Based on interviews and questionnaire surveys, we graphically visualized the structure of the food-sharing networks and the seasonality of the shared-food species. The study revealed the proportions of foods acquired through self-production, sharing networks and purchases by systematic food category, and quantified the monetary and nutritional values of the non-market foods. The island residents shared various seasonal foods within and beyond the island, and the non-market food was beneficial to their health. More than 20% of the islanders’ annual consumption of potatoes, vegetables, seafood, and fruits were obtained through the food-sharing networks. Non-market food largely saved the household expenditure and provided a wide variety of nutrients. As future perspectives of food-sharing networks, we suggest balancing market-based and non-market food provisions, promoting local production for local consumption, and designing local food resilience in disaster events.