{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Economic Activity\n", "**Table of contents**\n", "\n", "- Overview\n", "\n", "- Setup\n", "\n", " - Authentication Token\n", "\n", "- Query\n", "\n", " - Output Description\n", "\n", "- Related Links" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Overview\n", "\n", "Use the Economic Activity API to calculate emissions associated with investment activities using sector-based economic data. The endpoint supports emissions accounting aligned with Scope 3 Category 15 (Investments) under the GHG Protocol, enabling organizations to estimate financed emissions from investment portfolios based on the economic sectors of invested activities.\n", "\n", "In the API request, you provide the economic activity type, spending amount, and currency unit. The API response provides a breakdown of emissions by gas and an aggregate CO2e value." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup\n", "Ensure that Python 3+ is installed on your system.\n", "\n", "Note: To run this notebook, you must first add your credentials to `'../../../auth/secrets.ini'` in the following format:\n", "\n", "```\n", "[EAPI]\n", "api.pat_token = \n", "api.tenant_id = \n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Install the prerequisite Python packages.\n", "%pip install pandas configparser IPython requests" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import configparser\n", "import requests\n", "import json\n", "from IPython.display import display as display_summary" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Authentication Token\n", "Run the following code snippet to generate a Bearer Token by using your api_key configured in secrets.ini." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Authentication Success\n" ] } ], "source": [ "config = configparser.RawConfigParser()\n", "config.read(['../../../auth/secrets.ini','../../../auth/config.ini'])\n", "\n", "EAPI_PAT_TOKEN = config.get('EAPI', 'api.pat_token')\n", "EAPI_TENANT_ID = config.get('EAPI', 'api.tenant_id')\n", "EAPI_CLIENT_ID = 'ghgemissions-' + EAPI_TENANT_ID\n", "\n", "EAPI_AUTH_CLIENT_ID = 'saascore-' + EAPI_TENANT_ID\n", "EAPI_AUTH_ENDPOINT = config.get('EAPI', 'api.auth_endpoint')\n", "\n", "EAPI_BASE_URL = config.get('EAPI', 'api.base_url')\n", "EAPI_ENDPOINT = f\"{EAPI_BASE_URL}/economic-activity\"\n", "\n", "auth_request_headers: dict = {}\n", "auth_request_headers[\"X-IBM-Client-Id\"] = EAPI_AUTH_CLIENT_ID\n", "auth_request_headers[\"X-IBM-Envizi-Pat\"] = EAPI_PAT_TOKEN\n", "\n", "verify = True\n", "\n", "auth_url = f\"{EAPI_AUTH_ENDPOINT}\"\n", "\n", "response = requests.post(url = auth_url,\n", " headers = auth_request_headers,\n", " verify = verify\n", " )\n", "if response.status_code == 200:\n", " jwt_token = response.text\n", " print(\"Authentication Success\")\n", "else: \n", " print(\"Authentication Failed\")\n", " print(response.text)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Query\n", "The example request payload (below) queries IBM Envizi - Emissions API for the emissions generated from $12,345 USD spent on Accommodation services in New York, USA:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "payload = {\n", " \"activity\": {\n", " \"type\": \"Accommodation\",\n", " \"value\": 12345,\n", " \"unit\": \"usd\"\n", " },\n", " \"location\": {\n", " \"country\": \"USA\",\n", " \"stateProvince\": \"New York\"\n", " },\n", " \"time\": {\n", " \"date\": \"2022-01-01\"\n", " }\n", "}" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Create the query headers\n", "request_headers: dict = {}\n", "request_headers[\"Content-Type\"] = \"application/json\"\n", "request_headers[\"x-ibm-client-id\"] = EAPI_CLIENT_ID\n", "request_headers[\"Authorization\"] = \"Bearer \" + jwt_token\n", "\n", "# Submit the request\n", "response = requests.post(EAPI_ENDPOINT, \n", " headers = request_headers,\n", " data = json.dumps(payload))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For more information about allowable parameters for the payload, please see [Emissions API Developer Guide]()." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "\n" ] }, { "data": { "text/html": [ "
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transactionIdtotalCO2eCO2CH4assetTurnoverRatiounitdescription
0e27a5a51-3342-4d7c-87f9-281d43e6e3722283.8251938.165308.6250.77kgCO2eThe Accommodation emissions factor used to calculate this result was obtained from the US EPA/Climate Leaders + eGRID + USEEIO factor set for the Global region year 2022.
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" ], "text/plain": [ " transactionId totalCO2e CO2 CH4 \\\n", "0 e27a5a51-3342-4d7c-87f9-281d43e6e372 2283.825 1938.165 308.625 \n", "\n", " assetTurnoverRatio unit \\\n", "0 0.77 kgCO2e \n", "\n", " description \n", "0 The Accommodation emissions factor used to calculate this result was obtained from the US EPA/Climate Leaders + eGRID + USEEIO factor set for the Global region year 2022. " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "if response.text != \"\":\n", " # Get the response as json\n", " response_json = response.json()\n", " \n", " # Get json and convert to dataframe\n", " json_str = json.dumps(response_json)\n", " dict = json.loads(json_str)\n", " dataframe = pd.json_normalize(dict) \n", " \n", " # display\n", " print(\"\\n\\n\")\n", " pd.set_option('display.max_colwidth', None)\n", " display( dataframe) \n", "else:\n", " print(\"Empty Response\") " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Output Explanation\n", "transactionId - An Emissions API transaction id.\n", "\n", "\n", "\n", "totalCO2e - The total emissions as CO2 equivalent (CO2e)\n", "\n", "assetTurnoverRatio - An efficiency metric that measures how effectively a company utilizes its total assets to generate revenue relative to the carbon emissions produced\n", "\n", "co2 - The amount of CO2 (Carbon Dioxide) in the CO2e value.\n", "\n", "\n", "\n", "ch4 - The amount of CH4 (Methane) in the CO2e value.\n", "\n", "\n", "\n", "n2O - The amount of N2O (Nitrous Oxide) in the CO2e value.\n", "\n", "\n", "\n", "hfc - The amount of HFCs (Hydrofluorocarbons) in the CO2e value.\n", "\n", "\n", "\n", "pfc - The amount of PFCs (Perfluorocarbons) in the CO2e value.\n", "\n", "\n", "\n", "sf6 - The amount of SF6 (Sulphur Hexafluoride) in the CO2e value.\n", "\n", "\n", "\n", "nf3 - The amount of NF3 (Nitrogen Trifluoride) in the CO2e value.\n", "\n", "\n", "\n", "bioCo2 - The amount of bio CO2 in the CO2 value.\n", "\n", "\n", "\n", "indirectCo2e - The amount of CO2e that is indirect in the CO2e value.\n", "\n", "\n", "\n", "unit - The unit of measure of the values.\n", "\n", "\n", "\n", "description - A description of the source factor set of the factor used in the calculation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Related Links\n", "\n", "\n", "[Emissions API Developer Guide](https://developer.ibm.com/apis/catalog/ghgemissions--ibm-envizi-emissions-api/Introduction)" ] } ], "metadata": { "kernelspec": { "display_name": ".venv", "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": "3.12.10" } }, "nbformat": 4, "nbformat_minor": 4 }