Towards a more holistic sustainability assessment framework for agro-bioenergy systems — A review

The use of life cycle assessment (LCA) as a sustainability assessment tool for agro-bioenergy system usually has an industrial agriculture bias. Furthermore, LCA generally has often been criticized for being a decision maker tool which may not consider decision takers perceptions. They are lacking in spatial and temporal depth, and unable to assess sufficiently some environmental impact categories such as biodiversity, land use etc. and most economic and social impact categories, e.g. food security, water security, energy security. This study explored tools, methodologies and frameworks that can be deployed individually, as well as in combination with each other for bridging these methodological gaps in application to agro-bioenergy systems. Integrating agronomic options, e.g. alternative farm power, tillage, seed sowing options, fertilizer, pesticide, irrigation into the boundaries of LCAs for agro-bioenergy systems will not only provide an alternative agro-ecological perspective to previous LCAs, but will also lead to the derivation of indicators for assessment of some social and economic impact categories. Deploying life cycle thinking approaches such as energy return on energy invested-EROEI, human appropriation of net primary production-HANPP, net greenhouse gas or carbon balance-NCB, water footprint individually and in combination with each other will also lead to further derivation of indicators suitable for assessing relevant environmental, social and economic impact categories. Also, applying spatio-temporal simulation models has a potential for improving the spatial and temporal depths of LCA analysis.     

经济开发区危险废物排放权交易模

简要介绍污染物排放权交易制度的理论基础，根据经济开发区自身特点，提出其在危险废物减量化管理中运用的新思路，并通过数学模型，以控制总量、分配决定因子等为制约条件，发展了一套针对经济开发区危险废弃物排放权交易系统的计算方法。     

Advancing Energy Access Modelling with Geographic Information System Data

One of the main goals in pursuing sustainable development is to provide universal access to modern energy services, notably through the use of off-grid renewable energy technologies. To date, integrated assessment models (IAMs) poorly address energy access targets. In the context of research dedicated to energy scenarios and climate change mitigation in Africa, we attempt to advance the representation of energy access in one such IAM by using GIS data. In a case study for Ethiopia with the TIAM-ECN model, we demonstrate that by enriching an IAM with information derived from GIS databases, insights are obtained that better capture the dynamics of energy access developments, in comparison to conventional IAM analysis of energy technology deployment pathways. When duly accounting for the geographical spread in demography and technology costs in a developing country, we find that many people may gain access to electricity in remote areas thanks to the availability of affordable off-grid power production options that render expensive grid extensions unnecessary. This effect is not explicitly accounted for in most traditional IAMs. By the middle of the century, off-grid technologies could provide affordable electricity to 70% of the Ethiopian population, based almost entirely on renewable sources such as wind, solar and hydropower.     

Assessment of Climate Change Effects on Canada's National Park System

To estimate the magnitude of climate change anticipated forCanada's 38 National Parks (NPs) and Park Reserves, seasonaltemperature and precipitation scenarios were constructed for 2050and 2090 using the Canadian Centre for Climate Modelling andAnalysis (CCCma) coupled model (CGCM1). For each park, we assessed impacts on physical systems, species, ecosystems andpeople. Important, widespread changes relate to marine andfreshwater hydrology, glacial balance, waning permafrost, increased natural disturbance, shorter ice season, northern andupward altitudinal species and biome shifts, and changed visitation patterns. Other changes are regional (e.g., combinedEast coast subsidence and sea level rise increase coastal erosionand deposition, whereas, on the Pacific coast, tectonic upliftnegates sea level rise). Further predictions concern individualparks (e.g., Unique fens of Bruce Peninsular NP will migratelakewards with lowered water levels, but structural regulation of Lake Huron for navigation and power generation would destroythe fens). Knowledge gaps are the most important findings. Forexample: we could not form conclusions about glacial massbalance, or its effects on rivers and fjords. Likewise, for theEast Coast Labrador Current we could neither estimate temperature and salinity effects of extra iceberg formation, nor the further effects on marine food chains, and breeding park seabirds. We recommend 1) Research on specific large knowledge gaps; 2) Climate change information exchange with protected area agencies in other northern countries; and 3) incorporating climate uncertainty into park plans and management. We discuss options for a new park management philosophy in the face of massive change and uncertainty.     

Composite use of numerical groundwater flow modeling and geoinformatics techniques for monitoring Indus Basin aquifer, Pakistan

The integration of the Geographic Information System (GIS) with groundwater modeling and satellite remote sensing capabilities has provided an efficient way of analyzing and monitoring groundwater behavior and its associated land conditions. A 3-dimensional finite element model (Feflow) has been used for regional groundwater flow modeling of Upper Chaj Doab in Indus Basin, Pakistan. The approach of using GIS techniques that partially fulfill the data requirements and define the parameters of existing hydrologic models was adopted. The numerical groundwater flow model is developed to configure the groundwater equipotential surface, hydraulic head gradient, and estimation of the groundwater budget of the aquifer. GIS is used for spatial database development, integration with a remote sensing, and numerical groundwater flow modeling capabilities. The thematic layers of soils, land use, hydrology, infrastructure, and climate were developed using GIS. The Arcview GIS software is used as additive tool to develop supportive data for numerical groundwater flow modeling and integration and presentation of image processing and modeling results. The groundwater flow model was calibrated to simulate future changes in piezometric heads from the period 2006 to 2020. Different scenarios were developed to study the impact of extreme climatic conditions (drought/flood) and variable groundwater abstraction on the regional groundwater system. The model results indicated a significant response in watertable due to external influential factors. The developed model provides an effective tool for evaluating better management options for monitoring future groundwater development in the study area.     

The trophic position of dead autochthonous organic material and its treatment in trophic analyses

1. The importance of the recycling of organic matter for the overall carbon and nutrient flow in a food web, e.g., by the microbial loop has been recognized for pelagic and other ecosystems during the last decade. In contrast, analyses of the trophic food web structure conducted, e.g., by network analysis based on mass‐balanced flow diagrams (i.e., computation of, e.g., trophic positions and transfer efficiencies, organismal composition of trophic levels) which greatly contribute to our understanding of the flow and cycling of matter in food webs, have not yet responded adequately to this fact by developing coherent techniques with which dead organic matter and its consumers could be considered in the models. 2. At present, dead organic matter (measured in units of carbon or nutrients) is either allocated to a fixed trophic position (between zero and one), or the trophic position of dead autochthonous material depends on the trophic position of the organisms which released it. This causes partially ambiguous and inconsistent interpretations of key measures like trophic transfer efficiences and trophic positions and greatly hampers cross‐system comparisons. 3. The present paper describes and compares four different definitions of the trophic position of dead autochthonous organic material which have either been newly invented or already used. Their impact on the resulting trophic positions of individual groups is illustrated using a food web model from the pelagic zone of Lake Constance. The present analysis evaluates the partially far reaching consequences of the definition chosen, and suggests to allocate all dead organic material to the ‘zeroth’ trophic level irrespectively of its origin (allochthonous or autochthonous), chemical composition and the commodity used to quantify the food web model (e.g., units of carbon or nutrients). By this means trophic positions and trophic transfer efficiencies get a clear and consistent ecological interpretation, while inconsistencies between analyses conducted in units of carbon or nutrients and some operational problems can be overcome and cross‐system comparisons and empirical verification are facilitated.     

Monitoring sediment oxygen demand for assessment of dissolved oxygen distribution in river

Sediment oxygen demand (SOD) has become an integral part of modeling dissolved oxygen (DO) within surface water bodies. Because very few data on SOD are available, it is common for modeler to take SOD values from literature for use within DO models. SOD is such an important parameter in modeling DO that this approach may lead to erroneous results. This paper reported on developing an approach for monitoring sediment oxygen demand conducted with undisturbed sediment core samples, where the measured results were incorporated into a water quality model for simulating and assessing dissolved oxygen distribution in the Xindian River of northern Taiwan. The measured results indicate that a higher freshwater discharge results in a lower SOD. Throughout a 1-year observation in 2004, the measured SOD ranged from 0.367 to 1.246 g/m(2)/day at the temperature of 20°C. The mean values of the measured SOD at each station were adopted in a vertical two-dimensional water quality model to simulate the DO distribution along the Xindian River. The simulating results accurately depict the field-measured DO distribution during the low and high flow conditions. Model sensitivity analyses were also conducted with increasing and decreasing SOD values for the low and high flow conditions and revealed that SOD had a significant impact on the DO distribution along the Xindian River. The present work combined with field measurements and numerical simulation should assist in river water quality management.     

Influence of different digital terrain models (DTMs)on alpine permafrost modeling

The thawing of alpine permafrost due to changes in atmospheric conditions can have a severe impact, e.g., on the stability of rock walls. The energy balance model, PERMEBAL, was developed in order to simulate the changes and distribution of ground surface temperature (GST) in complex high-mountain topography. In such environments, the occurrence of permafrost depends greatly on the topography, and thus, the digital terrain model (DTM) is an important input of PERMEBAL. This study investigates the influence of the DTM on the modeling of the GST. For this purpose, PERMEBAL was run with six different DTMs. Five of the six DTMs are based on the same base data, but were generated using different interpolators. To ensure that only the topographic effect on the GST is calculated, the snow module was turned off and uniform conditions were assumed for the whole test area. The analyses showed that the majority of the deviations between the different model outputs related to a reference DTM had only small differences of up to 1 K, and only a few pixels deviated more than 1 K. However, we also observed that the use of different interpolators for the generation of a DTM can result in large deviations of the model output. These deviations were mainly found at topographically complex locations such as ridges and foot of slopes.     

Mechanistic Virtual Modeling: Coupling a Plant Simulation Model with a Three-dimensional Plant Architecture Component

The aim of this research is to integrate plant architectural modeling or “visualization modeling” and “mechanistic” or physiologically based modeling to describe how a real plant functions using a virtual crop. Virtual crops are life-like computer representations of crops based on individual plants and including the representation of the substrate on which the plants grow. The integration of a three-dimensional expression and the mechanistic model of plant development and growth requires the knowledge of the position of the organs along the different plant axes (the topology), their sizes, their forms, and their spatial orientation. The plant simulation model simulates the topology and organ weight or length. The superposition of spatial position and the topology produces the architecture of the plant. The association between sizes and organs creates what we refer to as the plant morphological model. Both components, the architectural model and the morphology model, are detailed in this paper. Once the integration is complete, the system produces a movie-like animation that shows the plant growing. The integrated model may simulate one or several plants growing simultaneously (in parallel). Visual capabilities make the proposed system very unique as it allows users to judge the results of the simulation the same way a farmer judges the situation of the crops in real life, by visually observing the field.     

Determining the global significance of local and regional mitigation strategies: Setting the scene with global integrated assessment models

Because of the importance and complexity of the processes involved in the response of land cover and land use to changing environmental conditions, other approaches are required to evaluate the effectiveness of mitigation options. One such approach is provided by the global integrated assessment model, IMAGE 2. This article presents the structure and some of the underlying assumptions of IMAGE 2, which illustrates the importance of feedback processes in evaluating the effectiveness of mitigation options with respect to land use. Although models such as IMAGE 2 are unsuitable for local and national mitigation evaluations, they can be used to define the regional and global constraints of the smaller scale assessments.     

Robust Energy Portfolios Under Climate Policy and Socioeconomic Uncertainty

Concerning the stabilization of greenhouse gases, the UNFCCC prescribes measures to anticipate, prevent, or minimize the causes of climate change and mitigate their adverse effects. Such measures should be cost-effective and scientific uncertainty should not be used as a reason for postponing them. However, in the light of uncertainty about climate sensitivity and other underlying parameters, it is difficult to assess the importance of different technologies in achieving robust long-term climate risk mitigation. One example currently debated in this context is biomass energy, which can be used to produce both carbon-neutral energy carriers, e.g., electricity, and at the same time offer a permanent CO₂ sink by capturing carbon from the biomass at the conversion facility and permanently storing it. We use the GGI Scenario Database IIASA [3] as a point of departure for deriving optimal technology portfolios across different socioeconomic scenarios for a range of stabilization targets, focusing, in particular, on new, low-emission scenarios. More precisely, the dynamics underlying technology adoption and operational decisions are analyzed in a real options model, the output of which then informs the portfolio optimization. In this way, we determine the importance of different energy technologies in meeting specific stabilization targets under different circumstances (i.e., under different socioeconomic scenarios), providing valuable insight to policymakers about the incentive mechanisms needed to achieve robust long-term climate risk mitigation.     

Decentralized control of units in smart grids for the support of renewable energy supply

Due to the significant environmental impact of power production from fossil fuels and nuclear fission, future energy systems will increasingly rely on distributed and renewable energy sources (RES). The electrical feed-in from photovoltaic (PV) systems and wind energy converters (WEC) varies greatly both over short and long time periods (from minutes to seasons), and (not only) by this effect the supply of electrical power from RES and the demand for electrical power are not per se matching. In addition, with a growing share of generation capacity especially in distribution grids, the top-down paradigm of electricity distribution is gradually replaced by a bottom-up power supply. This altogether leads to new problems regarding the safe and reliable operation of power grids. In order to address these challenges, the notion of Smart Grids has been introduced. The inherent flexibilities, i.e. the set of feasible power schedules, of distributed power units have to be controlled in order to support demand–supply matching as well as stable grid operation. Controllable power units are e.g. combined heat and power plants, power storage systems such as batteries, and flexible power consumers such as heat pumps. By controlling the flexibilities of these units we are particularly able to optimize the local utilization of RES feed-in in a given power grid by integrating both supply and demand management measures with special respect to the electrical infrastructure. In this context, decentralized systems, autonomous agents and the concept of self-organizing systems will become key elements of the ICT based control of power units. In this contribution, we first show how a decentralized load management system for battery charging/discharging of electrical vehicles (EVs) can increase the locally used share of supply from PV systems in a low voltage grid. For a reliable demand side management of large sets of appliances, dynamic clustering of these appliances into uniformly controlled appliance sets is necessary. We introduce a method for self-organized clustering for this purpose and show how control of such clusters can affect load peaks in distribution grids. Subsequently, we give a short overview on how we are going to expand the idea of self-organized clusters of units into creating a virtual control center for dynamic virtual power plants (DVPP) offering products at a power market. For an efficient organization of DVPPs, the flexibilities of units have to be represented in a compact and easy to use manner. We give an introduction how the problem of representing a set of possibly 10¹⁰⁰ feasible schedules can be solved by a machine-learning approach. In summary, this article provides an overall impression how we use agent based control techniques and methods of self-organization to support the further integration of distributed and renewable energy sources into power grids and energy markets.     

Investments in Power Generation Under Uncertainty—a MIP Specification and Large-Scale Application for EU

Investments in power generation constitute a typical budget allocation problem in the context of multiple objectives, while all factors influencing investor’s decisions for power plants are subject to considerable uncertainties. The paper introduces a multi-objective stochastic model designed to optimize budget allocation decisions for power generation in the context of risk aversion taking into account several sources of uncertainty, especially with regard to volatility of fossil fuel and electricity prices, technological costs, and climate policy variability. Probability distributions for uncertain factors influencing investment decisions are directly derived from the stochastic global energy model PROMETHEUS and thus they take into account complex interactions between variables in the systemic context. In order to fully incorporate stochastic characteristics of the problem, the model is specified as an optimization problem in which the probability that an objective exceeds a given threshold is maximized (risk aversion) subject to a set of deterministic and probabilistic constraints. The model is formulated as a mixed integer program providing complete flexibility on the joint distributions of rates of return of technologies competing for investments, as it can handle non-symmetric distributions and take automatically into account complex covariance patterns as emerging from comprehensive PROMETHEUS stochastic results. The analysis shows that risk is a crucial factor for power generation investments with investors not opting for technologies subject to uncertainty related to climate policies and fossil fuel prices. On the other hand, combination of options with negative covariance tends to benefit in the context of risk-hedging behavior.     

Confronting Management Challenges in Highly Uncertain Natural Resource Systems: a Robustness–Vulnerability Trade-off Approach

This paper presents a framework for the study of policy implementation in highly uncertain natural resource systems in which uncertainty cannot be characterized by probability distributions. We apply the framework to parametric uncertainty in the traditional Gordon–Schaefer model of a fishery to illustrate how performance can be sacrificed (traded-off) for reduced sensitivity and hence increased robustness, with respect to model parameter uncertainty. With sufficient data, our robustness–vulnerability analysis provides tools to discuss policy options. When less data are available, it can be used to inform the early stages of a learning process. Several key insights emerge from this analysis: (1) the classic optimal control policy can be very sensitive to parametric uncertainty, (2) even mild robustness properties are difficult to achieve for the simple Gordon–Schaefer model, and (3) achieving increased robustness with respect to some parameters (e.g., biological parameters) necessarily results in increased sensitivity (decreased robustness) with respect to other parameters (e.g., economic parameters). We thus illustrate fundamental robustness–vulnerability trade-offs and the limits to robust natural resource management. Finally, we use the framework to explore the effects of infrequent sampling and delays on policy performance.     

Environmental Exposure Assessment for Emergency Response in a Nuclear Power Plant Using an Integrated Source Term and 3D Numerical Model

Nuclear power plants are normally assumed to be safe when their radiation impact in all operational states is kept at a reasonably low level. However, accidentally released radioactive substances and ionizing radiation may lead to a situation that cannot maintain the regulatory prescribed dose limits for internal and external exposure of the personnel and population. Nuclear emergency preparedness and response in nuclear or radiological events have been of concern recently in international communities. Nuclear power plants may need to provide essential information regarding possible scenarios of accidental releases that might have short-term detrimental effects and long-term risks in nearby populated regions. This paper presents a synergistic integration of a source term model and a three-dimensional, time-dependent, numerical model (i.e., HOTMAC/RAPTAD), which was applied to simulate a specific scenario in which a vapor cloud was accidentally released from Maanshan (i.e., the third nuclear power plant) in South Taiwan. It aims at dealing with middle-range risk assessment for nuclear emergency preparedness and response. The solutions of such an integrated modeling platform can be found with numerical analyses that describe the processes of radionuclide generation, transport, decay, and deposition, giving the final risk assessment in a neighboring coastal city—Kaohsiung, South Taiwan. In addition, sensitivity analyses were performed to evaluate the internal consistency of model parameters, which further support the application potentials. Such a modeling technique is valuable because it can characterize the fate and transport of radioactive nuclides over the long term. The case study in South Taiwan uniquely demonstrates the feasibility and significance of such model integration.     

Inserting socio-environmental variables in the power sector expansion planning

Energy models consider technical and economic criteria to define the optimal energy mix to produce electricity. However, the results are sub-optimal from the social point of view, since they do not include socio-environmental factors. The purpose of this study is to access the sustainability of power plants considering technical-economic, socio-environmental and institutional aspects so that to improve the expansion planning. Thus, the efficiencies calculated by slacks based measure approach of Data Envelopment Analysis methodology can be implemented as constraints in the models, signalizing that the socio-environmental impact cannot exceed a limit, which can be calculated by the energy model itself. The results show that renewable power plants contribute more to allocative efficiency than fossil fuel ones, both in terms of social and environmental aspects, which are of utmost importance in ranking power plants to model power capacity expansion. Considering just technical and economic factors has proved to be insufficient to optimize the system in terms of allocative efficiency.     

An Integrated Assessment by Models for Energy Systems Analysis and Life-Cycle Assessment with a Case Study of Advanced Fossil-Fired Power Plants in China

We developed an integrated assessment (IA) using models for energy systems analysis and life-cycle assessment (LCA). Based on this assessment framework, we developed cost-benefit analysis (CBA) case studies for a hypothetical project designed to introduce advanced fossil-fired power generation technologies in China. Our MARKAL model for Japan confirmed that radical reductions (i.e., 80 % by 2050) of carbon dioxide (CO₂) could be attained from energy systems alone and that credit for emission allowances was required. We evaluated life-cycle costs and emissions of carbon dioxide, sulfur oxide, and nitrogen oxide gases for the energy technologies using an LCA model. Further, we applied a power generation planning model for six Chinese grids to provide a power mix structure, potentially producing credit by installing fossil-fired power generation technology and by using baseline grid emission factors with an average cost of electricity. Finally, by using dynamic emission reductions and additional costs from the two models, we conducted case studies of CBA for a hypothetical project to install the technologies in China. This was accomplished by evaluating emission reductions in monetary terms and by applying a life-cycle impact assessment model. A unique feature of our IA is its dynamic (time-varying) assessment of costs and benefits.     

2017年江苏省PM2.5数值模拟及内外源解析

根据江苏省72个国控点监测数据,采用了区域大气模式和多尺度空气质量模式系统(RAMS-CMAQ)模拟了2017年江苏省ρ(PM_2.5)的时空分布,耦合综合源追踪算法(ISAM)分析了不同地区排放源对ρ(PM_2.5)的贡献特征。结果表明,PM_2.5模拟与观测值的相关系数(r)=0.76,标准平均偏差(NMB)=5.2%,均方根误差(RMSE)=23.4μg/m₃,模拟结果落于观测结果0.5~2倍的比例(FAC2)=84.2%。源追踪模块结果显示,夏季主要受东南风控制,本地排放的贡献更大(省内贡献为52.34%),其他季节受偏北风输送影响,外源输送的影响较大(省外贡献为53.48%~56.84%);冬季苏北5市的排放贡献比沿江8市的更大,而春、夏季沿江8市排放贡献较大。     

How should environmental exposure risk be assessed? A comparison of four methods for exposure assessment of air pollutions

Methods commonly used to assess the environmental exposure risk at a location (e.g., proximity models) are usually based on different assumptions, leading to conflicting results and recommendations in epidemiological studies. In this case study, a comparative evaluation of the accuracy levels associated with four commonly used exposure risk estimate models [i.e., traditional proximity model (TPM), emission weighted proximity model (EWPM), the American Meteorological Society/EPA regulatory model (AERMOD), and ordinary kriging interpolation (OKI)] were conducted. Results show that at the annual and the monthly scales, the normalized exposure risk values simulated by AERMOD and EWPM have higher accuracy levels than the simulations from the TPM and OKI methods. However, AERMOD has higher accuracy than that of the EWPM, and this was attributed to the differences of input data. EWPM provided the most accurate simulations when analysts have access to only point emission source data. The results also indicate that the accuracies of the exposure risks simulated by AERMOD and EWPM can be influenced by factors such as the modeling extent, the distance settings, and so forth.     

Implications and concerns of deep-seated disposal of hydrocarbon exploration produced water using three-dimensional contaminant transport model in Bhit Area, Dadu District of Southern Pakistan

A three-dimensional contaminant transport model has been developed to simulate and monitor the migration of disposal of hydrocarbon exploration produced water in Injection well at 2,100 m depth in the Upper Cretaceous Pab sandstone, Bhit area in Dadu district of Southern Pakistan. The regional stratigraphic and structural geological framework of the area, landform characteristics, meteorological parameters, and hydrogeological milieu have been used in the model to generate the initial simulation of steady-state flow condition in the underlying aquifer’s layers. The geometry of the shallow and deep-seated characteristics of the geological formations was obtained from the drilling data, electrical resistivity sounding surveys, and geophysical well-logging information. The modeling process comprised of steady-state simulation and transient simulation of the prolific groundwater system of contamination transport after 1, 10, 30 years of injection. The contaminant transport was evaluated from the bottom of the injection well, and its short- and long-term effects were determined on aquifer system lying in varying hydrogeological and geological conditions.