{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# DiscRates class\n", "\n", "Discount rates are used to calculate the net present value of any future or past value. They are thus used to compare amounts paid (costs) and received (benefits) in different years. A project is economically viable (attractive), if the net present value of benefits exceeds the net present value of costs - a cost-benefit ratio < 1.\n", "\n", "There are several important implications that come along with discount rates. Namely, that higher discount rates lead to smaller net present values of future impacts (costs). As a consequence of that, climate action and mitigation measures can be postboned. In the literature higher discount rates are typically justified by the expectation of continued exponential growth of the economy.\n", "The most widley used interest rate in climate change economics is 1.4% as propsed by the Stern Review (2006). Neoliberal economists around Nordhaus (2007) claim that rates should be higher, around 4.3%. Environmental economists argue that future costs shouldn't be discounted at all. This is especially true for non-monetary variables such as ecosystems or human lifes, where no price tag should be applied out of ethical reasons. This discussion has a long history, reaching back to the 18th century: “Some things have a price, or relative worth, while other things have a dignity, or inner worth” (Kant, 1785).\n", "\n", "\n", "\n", "This class contains the discount rates for every year and discounts given values. Its attributes are:\n", "\n", " * years (np.array): years\n", " * rates (np.array): discount rates for each year (between 0 and 1)\n", "\n", "For a complete class documentation, refer to the Python modules docs: {py:class}`climada.entity.disc_rates.base.DiscRates`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An example of use - we define discount rates and apply them on a coastal protection scheme which initially costs 100 mn. USD plus 75'000 USD mainteance each year, starting after 10 years. Net present value of the project can be calculated as displayed:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2021-10-15T12:25:23.110277Z", "start_time": "2021-10-15T12:25:22.976995Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "net present value: 1.01231e+08\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import numpy as np\n", "from climada.entity import DiscRates\n", "\n", "# define discount rates\n", "years = np.arange(1950, 2100)\n", "rates = np.ones(years.size) * 0.014\n", "rates[51:55] = 0.025\n", "rates[95:120] = 0.035\n", "disc = DiscRates(years=years, rates=rates)\n", "disc.plot()\n", "\n", "# Compute net present value between present year and future year.\n", "ini_year = 2019\n", "end_year = 2050\n", "val_years = np.zeros(end_year - ini_year + 1)\n", "val_years[0] = 100000000 # initial investment\n", "val_years[10:] = 75000 # maintenance from 10th year\n", "npv = disc.net_present_value(ini_year, end_year, val_years)\n", "print(\"net present value: {:.5e}\".format(npv))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Read discount rates of an Excel file\n", "\n", "Discount rates defined in an excel file following the template provided in sheet `discount` of `climada_python/climada/data/system/entity_template.xlsx` can be ingested directly using the method `from_excel()`." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2021-10-15T12:25:33.821328Z", "start_time": "2021-10-15T12:25:33.333591Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Read file: /Users/ckropf/climada/data/entity_template.xlsx\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from climada.entity import DiscRates\n", "from climada.util import ENT_TEMPLATE_XLS\n", "\n", "# Fill DataFrame from Excel file\n", "file_name = ENT_TEMPLATE_XLS # provide absolute path of the excel file\n", "print(\"Read file:\", ENT_TEMPLATE_XLS)\n", "disc = DiscRates.from_excel(file_name)\n", "disc.plot();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Write discount rates" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Users may write the discounts in Excel format using write_excel() method `write_excel()`." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2021-10-15T12:25:45.026313Z", "start_time": "2021-10-15T12:25:44.966485Z" } }, "outputs": [], "source": [ "from climada.entity import DiscRates\n", "from climada.util import ENT_TEMPLATE_XLS\n", "\n", "# Fill DataFrame from Excel file\n", "file_name = ENT_TEMPLATE_XLS # provide absolute path of the excel file\n", "disc = DiscRates.from_excel(file_name)\n", "\n", "# write file\n", "disc.write_excel(\"results/tutorial_disc.xlsx\")" ] }, { "cell_type": "markdown", "id": "37bf8fe8", "metadata": {}, "source": [ "Pickle can always be used as well, but note that pickle has a [transient format](saving-with-pickle) and should be avoided when possible:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "from climada.util.save import save\n", "\n", "# this generates a results folder in the current path and stores the output there\n", "save(\"tutorial_disc.p\", disc)" ] } ], "metadata": { "hide_input": false, "kernelspec": { "display_name": "climada_env", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.16" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autoclose": false, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false }, "vscode": { "interpreter": { "hash": "4aebf7f26d9a9d4c9696d8ddcd034589cd11abb7fe515057c687f2f3cec840ea" } } }, "nbformat": 4, "nbformat_minor": 4 }