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AbstractBioenergy projects can lead to direct and indirect land use change (LUC), which can substantially affect greenhouse gas balances with both beneficial and adverse outcomes for bioenergy's contribution to climate change mitigation. The causes behind LUC are multiple, complex, interlinked, and change over time. This makes quantification uncertain and sensitive to many factors that can develop in different directions—including land use productivity, trade patterns, prices and elasticities, and use of by‐products associated with biofuels production. Quantifications reported so far vary substantially and do not support the ranking of bioenergy options with regard to LUC and associated emissions. There are however several options for mitigating these emissions, which can be implemented despite the uncertainties. Long‐rotation forest management is associated with carbon emissions and sequestration that are not in temporal balance with each other and this leads to mitigation trade‐offs between biomass extraction for energy use and the alternative to leave the biomass in the forest. Bioenergy's contribution to climate change mitigation needs to reflect a balance between near‐term targets and the long‐term objective to hold the increase in global temperature below 2°C (Copenhagen Accord). Although emissions from LUC can be significant in some circumstances, the reality of such emissions is not sufficient reason to exclude bioenergy from the list of worthwhile technologies for climate change mitigation. Policy measures to minimize the negative impacts of LUC should be based on a holistic perspective recognizing the multiple drivers and effects of LUC.This article is categorized under: Bioenergy > Economics and Policy Bioenergy > Climate and Environment

SYNOPSIS In Swaziland policies to alleviate fuelwood shortages have been low on the list of development priorities, as much of the land has extensive bush and tree cover. There are, however, major social and geographical imbalances in biomass resource supply—some areas of Swazi Nation Land are barren due to increasing population pressure and the subsequent demand for fuelwood. This paper will show, in detail, the methodology for the survey which was carried out in Swaziland to investigate the importance of accessibility in linking potential supply to demand. Physical location, land tenure and management practices are generally recognised as important factors to take into consideration when converting potential resources into realistic amounts of fuelwood supply. Guesswork, however, is often used to estimate the impacts of these factors on biomass supplies. A nation-wide study was made in order to obtain more information on the accessibility of resources on privately owned land; results of a household surv...

In recent years, sustainability concerns have gained increasing attention among countries and stakeholders worldwide. Towards the transition to sustainable rural development, the rural web framework (RWF) has become a consistent tool. Indicators from the RWF have been used to explore sustainable rural development for decision-making tasks, which improves the social, economic, and environmental performance of rural regions. However, the application of the RWF in studies is on the decline. Furthermore, there is a lack of literature reviews on the importance of the RWF and its relationship with different facets of sustainable development. We conducted a systematic literature review (SLR) (a) to explore how studies have used the RWF in the context of sustainable development and (b) to identify areas for further research. This study found that the RWF has mostly been used in developed countries, with fewer applications in developing countries. We suggest that there should be increased application of the RWF, particularly in developing countries, to broaden the rural web–sustainable development discourse and its relevance. This paper presents several areas where the indicators of the RWF can be applied to illustrate their relevance for policy decisions towards the achievement of the sustainable development goals (SDGs).

Although it is widely assumed that business activity is dependent on flows of ecosystem services (ES), little evidence is available with which to evaluate this contention. To address this knowledge gap, we conducted a questionnaire survey of business dependencies on twenty-six different ES in the English county of Dorset, where the environment supports a significant component of the local economy. Responses were received from 212 businesses across twenty-eight sectors. While virtually all businesses (98%) were familiar with the concept of ES, dependency on ES was highly divided with 50% of businesses surveyed claiming no dependence on any ES flows. The highest businesses dependencies reported in this study were for regulating services with the ES of water quality and waste water treatment being of particular importance to businesses. The results however, advised that greater efforts are needed in highlighting the indirect benefits provided by Dorset’s ecosystems, with eight business sectors (58% of respondents) claiming no or little dependence on supporting and habitat services including the ES of biodiversity, habitats for species and maintenance of genetic diversity. Many businesses also indicated little or no dependence on the globally important ES of pollination and soil condition, which may reflect a lack of awareness of dependencies occurring upstream of their value chains. At the sector level, businesses directly involved in protecting, extracting, or manufacturing raw materials were found to be more dependent on provisioning, regulatory and supporting ES than those operating in the service sector who favored cultural ES. These results highlight the value of assessing business dependencies on ES flows, which could usefully inform environmental management and accounting systems and improve monitoring of business performance, and thereby contribute to achievement of sustainability goals.

The feasibility of reliably generating bioenergy from forest biomass waste is intimately linked to supply chain and production processing costs. These costs are, at least in part, directly related to assumptions about the reliability and cost-efficiency of the machinery used along the forestry bioenergy supply chain. Although mechanization in forestry operations has advanced in the last 20 years, it is evident that challenges remain in relation to production capability, standardization of wood quality, and supply guarantee from forestry resources because of the age and reliability of the machinery. An important component in sustainable bioenergy from biomass supply chains will be confidence in consistent production costs linked to guarantees about harvest and haulage machinery reliability. In this context, this paper examines the issue of machinery maintenance and advances in machine learning and big data analysis that are contributing to improved intelligent prediction that is aiding supply chain reliability in bioenergy from woody biomass. The concept of “Industry 4.0” refers to the integration of numerous technologies and business processes that are transforming many aspects of conventional industries. In the realm of machinery maintenance, the dramatic increase in the capacity to dynamically collect, collate, and analyze data inputs including maintenance archive data, sensor-based monitoring, and external environmental and contextual variables. Big data analytics offers the potential to enhance the identification and prediction of maintenance (PdM) requirements. Given that estimates of costs associated with machinery maintenance vary between 20% and 60% of the overall costs, the need to find ways to better mitigate these costs is important. While PdM has been shown to help, it is noticeable that to-date there has been limited assessment of the impacts of external factors such as weather condition, operator experiences and/or operator fatigue on maintenance costs, and in turn the accuracy of maintenance predictions. While some researchers argue these data are captured by sensors on machinery components, this remains to be proven and efforts to enhance weighted calibrations for these external factors may further contribute to improving the prediction accuracy of remaining useful life (RUL) of machinery. This paper reviews and analyzes underlying assumptions embedded in different types of data used in maintenance regimes and assesses their quality and their current utility for predictive maintenance in forestry. The paper also describes an approach to building ‘intelligent’ predictive maintenance for forestry by incorporating external variables data into the computational maintenance model. Based on these insights, the paper presents a model for an intelligent predictive maintenance system (IPdM) for forestry and a method for its implementation and evaluation in the field.

Quantification of spatial and temporal changes in forest cover is an essential component of forest monitoring programs. Due to its cloud free capability, Synthetic Aperture Radar (SAR) is an ideal source of information on forest dynamics in countries with near-constant cloud-cover. However, few studies have investigated the use of SAR for forest cover estimation in landscapes with highly sparse and fragmented forest cover. In this study, the potential use of L-band SAR for forest cover estimation in two regions (Longford and Sligo) in Ireland is investigated and compared to forest cover estimates derived from three national (Forestry2010, Prime2, National Forest Inventory), one pan-European (Forest Map 2006) and one global forest cover (Global Forest Change) product. Two machine-learning approaches (Random Forests and Extremely Randomised Trees) are evaluated. Both Random Forests and Extremely Randomised Trees classification accuracies were high (98.1-98.5%), with differences between the two classifiers being minimal (<0.5%). Increasing levels of post classification filtering led to a decrease in estimated forest area and an increase in overall accuracy of SAR-derived forest cover maps. All forest cover products were evaluated using an independent validation dataset. For the Longford region, the highest overall accuracy was recorded with the Forestry2010 dataset (97.42%) whereas in Sligo, highest overall accuracy was obtained for the Prime2 dataset (97.43%), although accuracies of SAR-derived forest maps were comparable. Our findings indicate that spaceborne radar could aid inventories in regions with low levels of forest cover in fragmented landscapes. The reduced accuracies observed for the global and pan-continental forest cover maps in comparison to national and SAR-derived forest maps indicate that caution should be exercised when applying these datasets for national reporting.

The objective of this study is to evaluate the hydrological impacts of climate change on rainfall, temperature and streamflow in a west flowing river originating in the Western Ghats of India. The long-term trend analysis for 110 yr of meteorological variables (rainfall and temperature) was carried out using the modified Mann–Kendall trend test and the magnitude of the trend was quantified using the Sen’s slope estimator. The Regional Climate Model (RCM), COordinated Regional climate Downscaling EXperiment (CORDEX) simulated daily weather data of baseline (1951–2005) and future RCP 4.5 scenarios (2006–2060) were used to run the hydrological model, Soil and Water Assessment Tool (SWAT), in order to evaluate the effect of climate change on rainfall, temperature and streamflow. Significant changes were observed with regard to rainfall, which have shown decreasing trend at the rate of 2.63 mm per year for the historical and 8.85 mm per year for RCP 4.5 future scenarios. The average temperature was found to be increasing at $$0.10\,^{\circ }\hbox {C}$$ per decade for both historical and future scenarios. The impact of climate change on the annual streamflow yielded a decreasing trend at the rate of $$1.2\,\hbox {Mm}^{3}$$ per year and 2.56 $$\hbox {Mm}^{3},$$ respectively for the past and future scenarios. The present work also investigates the capability of SWAT to simulate the groundwater flow. The simulated results are compared with the recession limb of the hydrograph and were found to be reasonably accurate.

AbstractBecause of its low level of energy consumption and the small scale of its industrial development, the Tibet Autonomous Region has historically been excluded from China's reported energy statistics, including those regarding CO2 emissions. In this paper, we estimate Tibet's energy consumption using limited online documents, and we calculate the 2014 energy‐related and process‐related CO2 emissions of Tibet and its seven prefecture‐level administrative divisions for the first time. Our results show that 5.52 million tons of CO2 were emitted in Tibet in 2014; 33% of these emissions are associated with cement production. Tibet's emissions per capita amounted to 1.74 tons in 2014, which is substantially lower than the national average, although Tibet's emission intensity is relatively high at 0.60 tons per thousand yuan in 2014. Among Tibet's seven prefecture‐level administrative divisions, Lhasa City and Shannan Region are the two largest CO2 contributors and have the highest per capita emissions and emission intensities. The Nagqu and Nyingchi regions emit little CO2 due to their farming/pasturing‐dominated economies. This quantitative measure of Tibet's regional CO2 emissions provides solid data support for Tibet's actions on climate change and emission reductions.