The impact of sustainability criteria on the costs and potentials of bioenergy production – Applied for case studies in Brazil and Ukraine

The goal of this paper is to analyse the impact of the implementation of a certification system on the management system (costs) of and the availability of land (quantity) for bioenergy production. Twelve socio-economic areas of concern (food supply, child labour, (minimum) wages, employment, health care and education) and environmental areas of concern (soil erosion, depletion of fresh water resources, nutrient losses and soil nutrient depletion, pollution from chemicals and biodiversity) are included. Since there is no generally accepted definition of sustainability, a loose and strict set of criteria are defined. Short rotation coppice (SRC) production systems in Ukraine and South East Brazil in 2015 are taken as case studies. The results indicate that it seems feasible to produce biomass for energy purposes at reasonable cost levels and meeting strict sustainability criteria at the same time. The loose set of criteria has no impact on the costs of energy crop production, which are calculated to be 1.7 € GJ^?1 in Brazil and 2.1 € GJ^?1 in Ukraine. The strict set of criteria results in an increase of the costs of energy crop production by 42 % in Brazil and 14 % in Ukraine. In general, compliance with strict socio-economic criteria has a limited impact on the costs, because SRC is relatively labour extensive. Strict environmental criteria likely have a larger impact.     

Harmonising bioenergy resource potentials—Methodological lessons from review of state of the art bioenergy potential assessments

Published estimates of the potential of bioenergy vary widely, mainly due to the heterogeneity of methodologies, assumptions and datasets employed. These discrepancies are confusing for policy and it is thus important to have scientific clarity on the basis of the assessment outcomes. Such clear insights can enable harmonisation of the different assessments. This review explores current state of the art approaches and methodologies used in bioenergy assessments, and identifies key elements that are critical determinants of bioenergy potentials. We apply the lessons learnt from the review exercise to compare and harmonise a selected set of country based bioenergy potential studies, and provide recommendations for conducting more comprehensive assessments. Depending on scenario assumptions, the harmonised technical biomass potential estimates up to 2030 in the selected countries range from 5.2 to 27.3 EJ in China, 1.1 to 18.8 EJ in India, 2.0 to 10.9 EJ in Indonesia, 1.6 to 7.0 EJ in Mozambique and 9.3 to 23.5 EJ in the US. From the review, we observed that generally, current studies do not cover all the basic (sustainability) elements expected in an ideal bioenergy assessment and there are marked differences in the level of parametric detail and methodological transparency between studies. Land availability and suitability lack spatial detail and especially degraded and marginal lands are poorly evaluated. Competition for water resources is hardly taken into account and biomass yields are based mostly on crude ecological zoning criteria. A few studies take into account improvements in management of agricultural and forestry production systems, but the underlying assumptions are hardly discussed. Competition for biomass resources among the various applications is crudely analysed in most studies and key assumptions such as demographic dynamics, biodiversity protection criteria, etc. are not explicitly discussed. To facilitate more comprehensive bioenergy assessments, we recommend an integrated analytical framework that includes all the key factors, employs high resolution geo-referenced datasets and accounts for potential feedback effects.     

您的数据中心是否过冷？冷并不总是好事

任何人都知道数据中心需要保持凉爽，IT设备的最佳工作温度是在20℃到25℃之间，相对应的湿度在40％到55％之间。但是数据中心可能会过冷。     

Spatio-temporal uncertainty in Spatial Decision Support Systems: A case study of changing land availability for bioenergy crops in Mozambique

Spatial Decision Support Systems (SDSSs) often include models that can be used to assess the impact of possible decisions. These models usually simulate complex spatio-temporal phenomena, with input variables and parameters that are often hard to measure. The resulting model uncertainty is, however, rarely communicated to the user, so that current SDSSs yield clear, but therefore sometimes deceptively precise outputs. Inclusion of uncertainty in SDSSs requires modeling methods to calculate uncertainty and tools to visualize indicators of uncertainty that can be understood by its users, having mostly limited knowledge of spatial statistics. This research makes an important step towards a solution of this issue. It illustrates the construction of the PCRaster Land Use Change model (PLUC) that integrates simulation, uncertainty analysis and visualization. It uses the PCRaster Python framework, which comprises both a spatio-temporal modeling framework and a Monte Carlo analysis framework that together produce stochastic maps, which can be visualized with the Aguila software, included in the PCRaster Python distribution package. This is illustrated by a case study for Mozambique in which it is evaluated where bioenergy crops can be cultivated without endangering nature areas and food production now and in the near future, when population and food intake per capita will increase and thus arable land and pasture areas are likely to expand. It is shown how the uncertainty of the input variables and model parameters effects the model outcomes. Evaluation of spatio-temporal uncertainty patterns has provided new insights in the modeled land use system about, e.g., the shape of concentric rings around cities. In addition, the visualization modes give uncertainty information in an comprehensible way for users without specialist knowledge of statistics, for example by means of confidence intervals for potential bioenergy crop yields. The coupling of spatio-temporal uncertainty analysis to the simulation model is considered a major step forward in the exposure of uncertainty in SDSSs.     

European biomass resource potential and costs

The objective of this study is to assess the European (EU27⁺ and Ukraine) cost and supply potential for biomass resources. Three methodological steps can be distinguished (partly based on studies explained elsewhere in this volume) (i) an evaluation of the available ‘surplus’ land, (ii) a modeled productivity and (iii) an economic assessment for 13 typical bioenergy crops. Results indicate that the total available land for bioenergy crop production – following a ‘food first’ paradigm – could amount to 900 000 km² by 2030. Three scenarios were constructed that take into account different development directions and rates of change, mainly for the agricultural productivity of food production. Feedstock supply of dedicated bioenergy crop estimates varies between 1.7 and 12.8 EJ y^?1. In addition, agricultural residues and forestry residues can potentially add to this 3.1–3.9 EJ y^?1 and 1.4–5.4 EJ y^?1 respectively. First generation feedstock supply is available at production costs of 5–15 € GJ^?1 compared to 1.5–4.5 € GJ^?1 for second generation feedstocks. Costs for agricultural residues are 1–7 € GJ^?1 and forestry residues 2–4 € GJ^?1. Large variation exists in biomass production potential and costs between European regions, 280 (NUTS2) regions specified. Regions that stand out with respect to high potential and low costs are large parts of Poland, the Baltic States, Romania, Bulgaria and Ukraine. In Western Europe, France, Spain and Italy are moderately attractive following the low cost high potential criterion.     

Effects of technological learning on future cost and performance of power plants with CO2 capture

This paper demonstrates the concept of applying learning curves in a consistent manner to performance as well as cost variables in order to assess the future development of power plants with CO₂ capture. An existing model developed at Carnegie Mellon University, which had provided insight into the potential learning of cost variables in power plants with CO₂ capture, is extended with learning curves for several key performance variables, including the overall energy loss in power plants, the energy required for CO₂ capture, the CO₂ capture ratio (removal efficiency), and the power plant availability. Next, learning rates for both performance and cost parameters were combined with global capacity projections for fossil-fired power plants to estimate future cost and performance of these power plants with and without CO₂ capture. The results of global learning are explicitly reported, so that they can be used for other purposes such as in regional bottom-up models. Results of this study show that IGCC with CO₂ capture has the largest learning potential, with significant improvements in efficiency and reductions in cost between 2001 and 2050 under the condition that around 3100 GW of combined cycle capacity is installed worldwide. Furthermore, in a scenario with a strict climate policy, mitigation costs in 2030 are 26, 11, 19 €/t (excluding CO₂ transport and storage costs) for NGCC, IGCC, and PC power plants with CO₂ capture, respectively, compared to 42, 13, and 32 €/t in a scenario with a limited climate policy. Additional results are presented for IGCC, PC, and NGCC plants with and without CO₂ capture, and a sensitivity analysis is employed to show the impacts of alternative assumptions on projected learning rates of different systems.     

Large-scale bioenergy production from soybeans and switchgrass in Argentina: Part B. Environmental and socio-economic impacts on a regional level

The feasibility of deploying a socio-economic and environmental impact analysis for large-scale bioenergy production on a regional level is analyzed, based on a set of defined criteria and indicators. The analysis is done for La Pampa province in Argentina. The case study results in conclusions in how far the criteria can be verified ex ante based on available methodologies and data sources. The impacts are analyzed for two bioenergy chains (soybeans and switchgrass) for a set of defined land use scenarios. The carbon stock change for switchgrass ranges from 0.2 to 1.2 ton C/ha/year and for soybean from ?1.2 to 0 ton C/ha/year, depending on the scenario. The GHG emission reduction ranges from 88% to 133% for the switchgrass bioenergy chain (replacing coal or natural gas) and from 16% to 94% for the soybean bioenergy chain (replacing fossil fuel) for various lifetime periods. The annual soil loss, compared to the reference land use system is 2–10 ton/ha for the soybean bioenergy chain and 1–2 ton/ha for the switchgrass bioenergy chain. In total, nine sustainability principles are analyzed. In the case of switchgrass, most environmental benefits can be achieved when produced on suitable land of abandoned cropland. Soybean production for bioenergy shows a good overall sustainability performance if produced on abandoned cropland. The production of switchgrass on degraded grassland shows socio-economic and environmental benefits, which is not the case for soybean production. The production of bioenergy production on non-degraded grassland is not preferred. It is concluded that the scenario approach enables understanding of the complexity of the bioenergy chain and the underlying factors influencing the sustainability principles. It is difficult to give ex ante a final conclusion whether a bioenergy chain is sustainable or not as this depends not only on the previous land use system but also on other factors as the selection of the bioenergy crop, the suitable agroecological zone and the agricultural management system applied. The results also imply that it is possible to steer for a large part the sustainability performance of a bioenergy chain during project development and implementation. Land use planning plays a key role in this process.     

Optimizing the energy efficiency of conventional multi-cylinder dryers in the paper industry

The paper industry is, with about 6% of the total worldwide industrial energy use, an energy-intensive industry. The drying section is with approximately 50% the largest energy consumer in a paper mill, energy use in this section is mainly heat use. Several options to decrease heat use in conventional multi-cylinder drying sections are investigated, calculating the effect on energy use. Optimization measures include a) decreasing the amount of water evaporation by applying additives in higher consistencies and by lowering the water viscosity, b) decreasing the heat use of water evaporation by increasing the dew point temperature of the dryer and c) increasing the amount of heat recovery by using exhaust air to not only pre-heat the incoming air but also to increase process water temperatures. These could all be achieved by retrofitting and/or choosing different processing conditions in existing factories. The combined thermal heat saving potential due to the optimization actions is 1.3 GJ_h/t paper (or 32% of the drying section’s heat use) as compared to the reference situation.     

From the global efforts on certification of bioenergy towards an integrated approach based on sustainable land use planning

This paper presents an overview of 67 ongoing certification initiatives to safeguard the sustainability of bioenergy. Most recent initiatives are focused on the sustainability of liquid biofuels. Content-wise, most of these initiatives have mainly included environmental principles. Despite serious concerns in various parts of the world on the socio-economic impacts of bioenergy production, these are generally not included in existing bioenergy initiatives. At the same time, the overview shows a strong proliferation of standards. The overview shows that certification has the potential to influence direct, local impacts related to environmental and social effects of direct bioenergy production. Key recommendations to come to an efficient certification system include the need for further harmonization, availability of reliable data and linking indicators on a micro, meso and macro levels. Considering the multiple spatial scales, certification should be combined with additional measurements and tools on a regional, national and international level. The role of bioenergy production on indirect land use change (ILUC) is still very uncertain and current initiatives have rarely captured impacts from ILUC in their standards. Addressing unwanted LUC requires first of all sustainable land use production and good governance, regardless of the end-use of the product. It is therefore recommended to extend measures to mitigate impacts from LUC to other lands and feedstock.