Data and Optimisation model for space heating and committed emissions for the built environment

This dataset is used to arrive to the results presented in the paper `Reducing committed emissions of heating towards 2050: Analysis of scenarios for the insulation of buildings and the decarbonisation of electricity generation' from Kaandorp et al. (2022). The dataset consists of a Python code together with the input data used to run the code. The code is used to compute which technology mix is to be applied in a neighbourhood to optimally minimise the carbon emissions associated with space heating between 2030 and 2050. The neighbourhoods used in this study are 'Felix Meritis', 'Prinses Irenebuurt', and 'Molenwijk'. The model is run for scenarios which represents different rates of the insulation of buildings and the decarbonisation of electricity production between 2020 and 2050.<br>The python code requires the following data files (provided in this collection):- Address_Neigborhood_Heat_Demand.xlsx- Heat_technology.xlsx (or Heat_teachnology_highEFhydrogen.xlsx to run change the input of the emission factors related to hydrogen).- Scenario_Settings.xlsx<br>The data file 'Scenario_Setting.xlsx' is used for a first-order sensitivity analysis).<br>The code in 'post_processing.py' is used to process the output data from 'Address_Gurobi_scenario_loop_5y_timestep.py' (in this dataset) to facilitate analysis.

本数据集用于复现Kaandorp等人2022年发表的论文《面向2050年降低供暖承诺碳排放：建筑保温与电力脱碳情景分析》(Reducing committed emissions of heating towards 2050: Analysis of scenarios for the insulation of buildings and the decarbonisation of electricity generation)的研究成果。该数据集包含Python代码与运行该代码所需的输入数据。此代码用于计算2030年至2050年间，为最优降低街区空间供暖相关碳排放，应采用的技术组合方案。本研究涉及的街区为Felix Meritis、Prinses Irenebuurt和Molenwijk。模型针对2020年至2050年间不同的建筑保温速率及电力生产脱碳速率的情景开展运行。
该Python代码依赖本数据集提供的以下数据文件：
- Address_Neigborhood_Heat_Demand.xlsx
- Heat_technology.xlsx（若需调整与氢能相关的排放因子输入，可改用Heat_teachnology_highEFhydrogen.xlsx）
- Scenario_Settings.xlsx
其中数据文件Scenario_Setting.xlsx可用于一阶敏感性分析。
本数据集内的post_processing.py代码用于处理本数据集内的Address_Gurobi_scenario_loop_5y_timestep.py的输出数据，以辅助开展分析工作。