Revealing effective regional decarbonisation measures to limit global temperature increase in uncertain transition scenarios with machine learning techniques

Climate change mitigation scenarios generated by integrated assessment models have been extensively used to support climate change negotiations on the global stage. To date, most studies exploring ensembles of these scenarios focus on the global picture, with more limited attention to regional metrics. A systematic approach is still lacking to improve the understanding of regional heterogeneity, highlighting key regional decarbonisation measures and their relative importance for meeting global climate goals under deep uncertainty. This study proposes a novel approach to gaining robust insights into regional decarbonisation strategies using machine learning techniques based on the IPCC SR1.5 scenario database. Random forest analysis first reveals crucial metrics to limit global temperature increases. Logistic regression modelling and the patient rule induction method are then used to identify which of these metrics and their combinations are most influential in meeting climate goals below 2 °C or below 1.5 °C. Solar power and sectoral electrification across all regions have been found to be the most effective measures to limit temperature increases. To further limit increase below 1.5 °C and not only 2 °C, decommissioning of unabated gas plants should be prioritised along with energy efficiency improvements. Bioenergy and wind power show higher regional heterogeneity in limiting temperature increases, with lower influences than aforementioned measures, and are especially relevant in Latin America (bioenergy) and countries of the Organisation for Economic Co-operation and Development and the Former Soviet Union (bioenergy and wind). In the future, a larger scenario ensemble can be applied to reveal more robust and comprehensive insights

Methods for simulation, planning, and operation of Aquifer Thermal Energy Storage under deep uncertainty

The building sector currently accounts for approximately one-third of the global demand for energy, and one-fifth of all energy-related greenhouse gas emissions (GHG). The development and adoption of energy-efficient technologies in this sector is therefore a key element towards efforts for the mitigation of climate change. In particular, heating is the single largest end use of energy in buildings; basic trends towards urbanization, as well as climate change, are also expected to significantly increase the demand of energy for cooling by the middle of the century. Energy technologies which can address both of these aspects are thus particularly promising. In this context, Aquifer Thermal Energy Storage (ATES) is an increasingly popular shallow geothermal energy technology. This method uses natural aquifer formations to seasonally store energy for heating and cooling, using “warm” and “cold” storage wells combined with a heat pump. This approach can reduce energy demand by more than half in larger buildings. ATES is used in nearly one-tenth of new commercial and utility buildings in the Netherlands, where suitable aquifers – combined with increasing demand for energy-efficient technologies – make the technology especially competitive. However, this growth has already...Policy Analysi

Mautam famines in Mizoram: An exploratory system dynamics approach

Mizoram, a state in the Northeast of India, is affected every half-century by cycles of crop damages and famines. These events - locally known as Mautam - have been hypothesized to follow the periodic flowering of bamboo forests and subsequent rodent outbreaks. As such, the 1958-1960 Mautam resulted in a significant loss of lives; more recently, a 2007-2008 outbreak caused heavy damages to crops. However, the dynamics of the bamboo and rodent ecosystems remain poorly understood, as are their interrelationships with Mizoram’s agriculture. This draft paper therefore presents an exploratory System Dynamics model of Mizoram’s Mautam phenomenon, focusing on the application of a systematic framework for uncertainty analysis. Furthermore, a representative set of policies was tested under deep uncertainty to evaluate possible outcomes. Preliminary results indicate that although the model is highly sensitive to the properties of the human and rodent population subsystems, emphasizing market connectivity to facilitate food imports may be a promising and robust policy.Multi Actor SystemsTechnology, Policy and Managemen

Improving scenario discovery for handling heterogeneous uncertainties and multinomial classified outcomes

HarvestOld - TPM-MAS-BA Beleidsanalys

Pynetlogo: Linking netlogo with python

Methods for testing and analyzing agent-based models have drawn increasing attention in the literature, in the context of efforts to establish standard frameworks for the development and documentation of models. This process can benefit from the use of established software environments for data analysis and visualization. For instance, the popular NetLogo agent-based modelling software can be interfaced with Mathematica and R, letting modellers use the advanced analysis capabilities available in these programming languages. To extend these capabilities to an additional user base, this paper presents the pyNetLogo connector, which allows NetLogo to be controlled from the Python general-purpose programming language. Given Python’s increasing popularity for scientific computing, this provides additional flexibility for modellers and analysts. PyNetLogo’s features are demonstrated by controlling one of NetLogo’s example models from an interactive Python environment, then performing a global sensitivity analysis with parallel processing.Policy Analysi

Tree-based ensemble methods for sensitivity analysis of environmental models: A performance comparison with Sobol and Morris techniques

Complex environmental models typically require global sensitivity analysis (GSA) to account for non-linearities and parametric interactions. However, variance-based GSA is highly computationally expensive. While different screening methods can estimate GSA results, these techniques typically impose restrictions on sampling methods and input types. As an alternative, this work evaluates two decision tree-based methods to approximate GSA results: random forests, and Extra-Trees. These techniques are applicable with common sampling methods, and continuous or categorical inputs. The tree-based methods are compared to reference Sobol GSA and Morris screening techniques, for three cases: an Ishigami-Homma function, a H1N1 pandemic model, and the CDICE integrated assessment model. The Extra-Trees algorithm performs favorably compared to Morris elementary effects, accurately approximating the relative importance of Sobol total effect indices. Furthermore, Extra-Trees can estimate variable interaction importances using a pairwise permutation measure. As such, this approach could offer a user-friendly option for screening in models with inputs of mixed types.Policy Analysi

Planning ATES systems under uncertainty

Old - TPM-MAS-BA BeleidsanalyseWater Resource

A control-oriented model for combined building climate comfort and aquifer thermal energy storage system

This paper presents a control-oriented model for combined building climate comfort and aquifer thermal energy storage (ATES) system. In particular, we first provide a description of building operational systems together with control framework variables. We then focus on the derivation of an analytical model for ATES system dynamics. The dynamics of stored thermal energy over time in each well of an ATES system is the most important concept for a building climate control framework. This concept is proportional to the volume and temperature of water in each well of an ATES system at each sampling time. In this paper we develop a novel mathematical model for both dynamical behavior of volume and temperature of water in each well of an ATES system and provide detailed steps for estimating the model parameters. To illustrate the applicability of our proposed model, a comparison based on an extensive simulation study using an aquifer groundwater simulation environment (MODFLOW) is provided

Methods for planning of ATES systems

Aquifer Thermal Energy Storage (ATES) systems contribute to reducing fossil energy consumption by providing sustainable space heating and cooling for buildings by seasonal storage of heat. ATES is important for the energy transition in many urban areas in North America, Europe and Asia. Despite the modest current ATES adoption level of about 0.2% of all buildings in the Netherlands, ATES subsurface space use has already grown to congestion levels in many Dutch urban areas. This problem is to a large extent caused by the current planning and permitting approach, which uses too spacious safety margins between wells and a 2D rather than 3D perspective. The current methods for permitting and planning of ATES do not lead to optimal use of available subsurface space, and, therefore, prevent realization of the expected contribution of the reduction of greenhouse gas (GHG) emissions by ATES. Optimal use of subsurface space in dense urban settings can be achieved with a coordinated approach towards the planning and operation of ATES systems, so-called ATES planning. This research identifies and elaborates crucial practical steps to achieve optimal use of subsurface space that are currently missing in the planning method. Analysis from existing ATES plans and exploratory modeling, coupling agent-based and groundwater models were used to demonstrate that minimizing GHG emissions requires progressively stricter regulation with intensifying demand for ATES. The simulations also quantified both the thresholds beyond which such stricter rules are needed as well as the effectiveness of different planning strategies, which can now effectively be used for ATES planning in practice. The results provide scientific insight in how technical choices in ATES well design, location and operation affect optimal use of subsurface space, and what trade-offs exist between the energy efficiency of individual systems and the combined reduction of the GHG emissions from a plan area. The presented ATES planning method following from the obtained insights now fosters practical planning and design rules suitable to ensure optimal and sustainable use of subsurface space – that is, maximizing GHG emission reductions by accommodating as many ATES systems as possible in the available aquifer, while maintaining a high efficiency for the individual ATES systems.Water ResourcesPolicy Analysi

Improved performance of heat pumps helps to use full potential of subsurface space for Aquifer Thermal Energy Storage

The application of seasonal Aquifer Thermal Energy Storage (ATES) contributes to meet goals for energy savings and greenhouse gas (GHG) emission reductions. Heat pumps have a crucial position in ATES systems because they dictate the operation scheme of the ATES wells and therefore play an important role in utilizing the storage potential of the subsurface.In the Netherlands, suitable climatic and geohydrological conditions in combination with progressive building energy efficiency regulation have caused the adoption of ATES to take off, resulting in a situation where demand for ATES exceeds the available subsurface space in many urban areas. The most important aspects in this problem are A) the permanent and often unused claim resulting from static permits for ATES operation, and B) excessive safety zones around wells to prevent interaction between wells. Both aspects result in an artificial reduction of subsurface space for potential new ATES systems. Recent research has shown that ATES systems could be placed much closer to each other, and that a controlled/limited degree of interaction between them can actually benefit the overall energy savings of an entire area.Two different simulation experiments were carried out to evaluate the effect of an adaptive permit capacity policy, as well as revised layout guidelines for ATES wells. Our solution provides a framework in which smaller distances between wells and adaptability of the permit volume plays a key role, to allow for optimal utilization of subsurface space for ATES and maximize GHG emission reduction. This paper shows how the total GHG emission reduction of an area can be increased by intensifying the use of the aquifer by allowing (some) interaction between ATES wells, which opens up unused but claimed subsurface space, and increase the number of heat pumps and ATES systems installed.Water ResourcesPolicy AnalysisTeam Tamas Keviczk