SDG_alignment_with_DAX_companies_Swisstext_2023.ipynb

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      "source": [
        "In this notebook, we use the technique of [sentence embeddings](https://paperswithcode.com/task/sentence-embedding) to analyse the SDG alignment of German DAX companies, as reflected by communications by/about these companies. After trying the basic analyses presented here, we encourage users to dive into more advanced questions related to sustainability and greenwashing.\n",
        "\n",
        "The dataset used in this notebook on be [downloaded on Kaggle](https://www.kaggle.com/datasets/equintel/dax-esg-media-dataset).\n"
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        "# In this cell, set DATA_DIR to the directory from which both files can be read.\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive/', force_remount=True)\n",
        "\n",
        "DATA_DIR = \"/content/gdrive/MyDrive/dax_esg_dataset/\""
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          "text": [
            "Mounted at /content/gdrive/\n"
          ]
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    {
      "cell_type": "code",
      "source": [
        "# Make sure that all dependencies are installed.\n",
        "import pandas as pd\n",
        "import os\n",
        "import numpy as np\n",
        "import sentence_transformers\n",
        "import torch\n",
        "import plotly.graph_objects as go"
      ],
      "metadata": {
        "id": "5tPCv7_7ioJD"
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      "execution_count": null,
      "outputs": []
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    {
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      "source": [
        "## 1. Loading the data"
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      "cell_type": "code",
      "source": [
        "esg_documents_df = pd.read_csv(os.path.join(DATA_DIR, \"esg_documents_for_dax_companies.csv\"), sep=\"|\")\n",
        "esg_documents_df.head()"
      ],
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          "height": 354
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        "id": "2hQIgYKfiVOY",
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              "   Unnamed: 0              company  \\\n",
              "0           2        Beiersdorf AG   \n",
              "1           3  Deutsche Telekom AG   \n",
              "2           5           Vonovia SE   \n",
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              "   internal symbol                                         title  url  \n",
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      "source": [
        "# form texts by concatenating title and content\n",
        "esg_texts = esg_documents_df.apply(lambda row: \" \".join([str(row[\"title\"]), str(row[\"content\"])]), axis=1)\n",
        "esg_texts[0][:100]"
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              "'BeiersdorfAG Sustainability Report 2021 Sustainability Highlight Report CARE BEYOND SKIN 2021 03 For'"
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        "sdg_df = pd.read_csv(os.path.join(DATA_DIR, \"sdg_descriptions_with_targetsText.csv\"))\n",
        "sdg_df.head()"
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              "0   1                  No Poverty   \n",
              "1   2                 Zero Hunger   \n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2de7663c-b16b-484c-ab17-16aed5e7e5b8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2de7663c-b16b-484c-ab17-16aed5e7e5b8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sdg_texts = sdg_df.apply(lambda row: \" \".join([row[\"name\"], row[\"description\"], row[\"targets\"], row[\"progress\"]]), axis=1)\n",
        "sdg_texts[0][:100]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "5peAiqVMiEZk",
        "outputId": "ae437f16-7647-4901-d22f-154098cd7499"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"No Poverty End poverty in all its forms everywhere ['1.1', 'By 2030, eradicate extreme poverty for a\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "companies = sorted(esg_documents_df.company.unique())\n",
        "companies"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pXyUZI8zjKwr",
        "outputId": "968e43de-fee1-4d4a-db63-4ac18e6cf83e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Beiersdorf AG', 'Deutsche Telekom AG', 'Vonovia SE', 'Merck KGaA',\n",
              "       'MTU', 'E ONSE', 'RWE AG', 'Heidelberg Cement AG', 'Siemens AG',\n",
              "       'Linde', 'Qiagen', 'Henkel', 'Daimler AG', 'Continental AG',\n",
              "       'Bayer AG', 'Volkswagen AG', 'Fresenius', 'Symrise AG',\n",
              "       'Sartorius AG', 'Porsche', 'SAP', 'Adidas AG', 'Deutsche Bank AG',\n",
              "       'Puma SE', 'Siemens Healthineers AG', 'Airbus SE', 'Covestro AG',\n",
              "       'Allianz SE', 'Infineon Technologies AG', 'BMW', 'Hannover R AG',\n",
              "       'Siemens Energy', 'Zalando SE',\n",
              "       'Muenchener Rueckversicherungs Gesellschaft AGin Muenchen',\n",
              "       'Deutsche Post AG', 'BASF SE', 'Deutsche Boerse AG', 'Brenntag',\n",
              "       'AkzoNobelNV', 'Vonovia'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Build embeddings"
      ],
      "metadata": {
        "id": "dNNkjV26jTeb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "# load the retriever model from huggingface model hub\n",
        "retriever = sentence_transformers.SentenceTransformer(\"flax-sentence-embeddings/all_datasets_v3_mpnet-base\", device=device)\n",
        "retriever"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cposkUb2jczo",
        "outputId": "655e8331-8bfc-42c3-b18c-3b5403cff9aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SentenceTransformer(\n",
              "  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: MPNetModel \n",
              "  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})\n",
              "  (2): Normalize()\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "company_text_embeddings = retriever.encode(esg_texts)\n",
        "sdg_embeddings = retriever.encode(sdg_texts)"
      ],
      "metadata": {
        "id": "eScCQp_Xjh-9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. SDG alignment of the DAX companies"
      ],
      "metadata": {
        "id": "F0Xbo3RojvFW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def cosine_similarity(embedding1, embedding2):\n",
        "    sim = np.dot(embedding1, embedding2)/(np.linalg.norm(embedding1)*np.linalg.norm(embedding2))\n",
        "    return sim"
      ],
      "metadata": {
        "id": "GFXu5I-wjxaC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We model SDG alignment as similarity between the company-related texts and the SDG descriptions. In this section, we first define the similarity function using standard cosine similarity. Then, we demonstrate some possible alignment analyses, their visualisations and interpretations. Finally, we provide suggestions for further, more advanced alignment analyses. "
      ],
      "metadata": {
        "id": "SNP1rhbJkD0g"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Most relevant SDGs for DAX companies"
      ],
      "metadata": {
        "id": "9-UxM_mbkeZb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's first consider the overall relevance of the 17 SDGs for all DAX companies taken together. We first compute an embedding that averages over all company texts. Then, we compare this embedding with the embeddings of the SDGs."
      ],
      "metadata": {
        "id": "eST_Xxp5koxB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "all_companies_embedding = np.mean(company_text_embeddings, axis=0)"
      ],
      "metadata": {
        "id": "E9Pqsc2qlAmN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sdg_relevance_scores = [cosine_similarity(all_companies_embedding, sdg_embedding) for sdg_embedding in sdg_embeddings]\n",
        "sdg_relevance_series = pd.Series(sdg_relevance_scores, index=sdg_df[\"name\"])\n",
        "sdg_relevance_series.sort_values(inplace=True)\n",
        "sdg_relevance_series.head()"
      ],
      "metadata": {
        "id": "E-_hK2U6lEPm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1d73afa3-a74e-4ff7-8e86-3b8d9d9487ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "name\n",
              "Gender Equality                           0.166441\n",
              "Peace, Justice and Strong Institutions    0.174364\n",
              "Life On Land                              0.204423\n",
              "Quality Education                         0.228199\n",
              "Zero Hunger                               0.259333\n",
              "dtype: float32"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = go.Figure(data=[go.Bar(x=sdg_relevance_series, y=[sdg+\" \" for sdg in sdg_relevance_series.index], orientation='h')])\n",
        "# Change the bar mode\n",
        "fig.update_layout(height=600, width=750, title=\"SDG relevance for DAX index\")\n",
        "fig.update_xaxes(title=\"Relevance\")\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "JUxFAwDJlR7j",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "outputId": "cb77dbc2-b264-4bfa-ef61-59c2530ec7b2"
      },
      "execution_count": null,
      "outputs": [
        {
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    {
      "cell_type": "markdown",
      "source": [
        "### Most relevant SDGs for a specific company, contrasting internal and external data"
      ],
      "metadata": {
        "id": "4OvS81uJlfXK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this analysis, we focus on a specific company which is defined using the COMPANY variable. We look up the \"internal\" and \"external\" embeddings for this company, average them and measure their similarity with each of the SDGs."
      ],
      "metadata": {
        "id": "69Oe3uMIuHiY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "COMPANY = \"Brenntag\"\n",
        "\n",
        "internal_company_indices = esg_documents_df[(esg_documents_df.company == COMPANY) & (esg_documents_df.internal == 1)].index\n",
        "internal_company_embedding = np.mean(company_text_embeddings[internal_company_indices], axis=0)\n",
        "\n",
        "external_company_indices = esg_documents_df[(esg_documents_df.company == COMPANY) & (esg_documents_df.internal == 0)].index\n",
        "external_company_embedding = np.mean(company_text_embeddings[external_company_indices], axis=0)\n",
        "\n",
        "company_sdg_relevance_scores = [[cosine_similarity(internal_company_embedding, sdg_embedding), \n",
        "                                 cosine_similarity(external_company_embedding, sdg_embedding)] for sdg_embedding in sdg_embeddings]\n",
        "company_sdg_relevance_df = pd.DataFrame.from_records(company_sdg_relevance_scores, index=sdg_df[\"name\"], columns=[\"internal\", \"external\"])\n",
        "company_sdg_relevance_df.sort_values(\"internal\", inplace=True)\n",
        "company_sdg_relevance_df.head()"
      ],
      "metadata": {
        "id": "2NhuxJT7leDu",
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          "height": 238
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        "outputId": "873295bb-93ef-4d25-d483-ac1d28cf9fdb"
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      "execution_count": null,
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              "                                        internal  external\n",
              "name                                                      \n",
              "Gender Equality                         0.107339  0.093954\n",
              "Peace, Justice and Strong Institutions  0.135041  0.130411\n",
              "Good Health and Well-being              0.153959  0.203413\n",
              "Zero Hunger                             0.155495  0.193238\n",
              "Climate Action                          0.160574  0.240062"
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          "metadata": {},
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        "fig = go.Figure(data=[\n",
        "    go.Bar(name='Internal', x=company_sdg_relevance_df[\"internal\"], y=company_sdg_relevance_df.index, orientation='h'),\n",
        "    go.Bar(name='External', x=company_sdg_relevance_df[\"external\"], y=company_sdg_relevance_df.index, orientation='h')\n",
        "])\n",
        "\n",
        "fig.update_layout(barmode='group', height=600, width=750, title=f\"SDG Relevance for {COMPANY}\")\n",
        "fig.update_xaxes(title=\"Relevance\")\n",
        "fig.show()"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next steps to further explore the data: modify the chart to answer the following questions:\n",
        "\n",
        "- What are the internally most important SDGs for BMW?\n",
        "- What are the most important SDGs for BMW when we take into account both the internal and the external data?\n",
        "- What are the SDGs for BMW with the largest relevance gap between internal and external data?"
      ],
      "metadata": {
        "id": "pEZ7MiAwszvO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Heatmap of SDG relevance for all companies"
      ],
      "metadata": {
        "id": "b51sgUHlmZ-T"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this analyses, we look at the alignment of all companies with the 17 SDGs, creating an overview over the DAX index."
      ],
      "metadata": {
        "id": "gBXqK6vgues9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "company_embeddings = []\n",
        "for company in companies:\n",
        "    company_indices = esg_documents_df[esg_documents_df.company == company].index\n",
        "    company_embedding = np.mean(company_text_embeddings[company_indices], axis=0)\n",
        "    company_embeddings.append(company_embedding)"
      ],
      "metadata": {
        "id": "phqc77AgmlkP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "company_records = []\n",
        "\n",
        "for company_embedding in company_embeddings:\n",
        "    company_record = []\n",
        "    for sdg_embedding in sdg_embeddings:\n",
        "        company_record.append(cosine_similarity(company_embedding, sdg_embedding))\n",
        "    company_records.append(company_record)"
      ],
      "metadata": {
        "id": "16hYQs3PyFqd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "heatmap_array = np.array(company_records)\n",
        "heatmap_array.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6MG3M3eusaGx",
        "outputId": "b2499421-7a6c-4418-e138-24946138fa73"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(40, 17)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = go.Figure(data=go.Heatmap(\n",
        "        z=heatmap_array,\n",
        "        x=sdg_df[\"name\"].tolist(),\n",
        "        y=[company + \" \" for company in companies],\n",
        "        colorscale='Viridis'))\n",
        "\n",
        "fig.update_layout(height=1000)\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ibKB-T5CsleV",
        "outputId": "7f96de17-7524-4ea2-bd1b-2f5747e67241"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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      "source": [
        "Next steps to further explore the data: modify the chart to answer the following questions:\n",
        "\n",
        "- What are the companies that are most aligned with the SDGs? (sort by sum of rows)\n",
        "- Which SDGs are most relevant for the DAX index? (sort by sum of columns)"
      ],
      "metadata": {
        "id": "GL7m-zActYKI"
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sBKF8POTClt_"
      },
      "execution_count": null,
      "outputs": []
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  ]
}