Tetap teratur dengan koleksi
Simpan dan kategorikan konten berdasarkan preferensi Anda.
Membuat dasbor atau analisis kustom untuk data Jejak Karbon Anda
Selain tampilan default dan ekspor data Jejak Karbon, Anda dapat membuat dasbor dan analisis kustom menggunakan data yang diekspor. Fleksibilitas ini memungkinkan visualisasi dan perincian data yang disesuaikan, sehingga memungkinkan insight yang lebih mendalam untuk mengoptimalkan pengelolaan jejak karbon Anda sesuai dengan persyaratan spesifik Anda.
Menggunakan Google Spreadsheet
Ikuti langkah-langkah berikut untuk membuat dasbor kustom untuk data Jejak Karbon Anda menggunakan Google Spreadsheet.
Ikuti langkah-langkah berikut untuk membuat dasbor Looker kustom untuk data Jejak Karbon Anda.
Opsi ini direkomendasikan jika Anda adalah pelanggan Looker lama.
Estimasi data emisi pada tingkat perincian yang berbeda dengan menggabungkan data Jejak Karbon dengan data Penagihan Cloud
Anda dapat menggabungkan data ekspor Jejak Karbon dengan data ekspor Penagihan Cloud untuk melihat emisi karbon Anda pada berbagai tingkat perincian. Hal ini memungkinkan analisis pada
tingkat label, tag, atau resource yang disesuaikan, yang mendukung identifikasi
area untuk mengurangi dampak lingkungan Anda. Anda dapat mengikuti langkah-langkah ini untuk memperkirakan data emisi tingkat instance dan data emisi tingkat tag atau label.
Gabungkan Set Data: Pertama, gabungkan data biaya penggunaan mendetail Penagihan Cloud per jam ke tingkat bulanan, yang dikelompokkan menurut ID akun penagihan, project, produk, resource, dan region sebelum digabungkan. Kemudian, gabungkan data Jejak Karbon yang diekspor dengan data biaya penggunaan mendetail Penagihan Cloud menggunakan dimensi umum ID akun penagihan, project, produk, region, dan bulan.
Memperkirakan Emisi Tingkat Resource: Memecah data emisi karbon (digabungkan berdasarkan ID akun penagihan, project, produk, region, dan bulan) ke tingkat resource individual. Estimasi ini dapat dilakukan dengan mendistribusikan emisi secara proporsional berdasarkan kontribusi setiap resource terhadap biaya daftar dalam ID akun penagihan, project, produk, wilayah, dan bulan tertentu. Penggunaan cost_at_list akan menghindari efek samping dari potensi diskon harga. Terapkan langkah-langkah validasi untuk mengidentifikasi dan memitigasi potensi penghitungan ganda emisi.
Catatan Penting: Emisi tingkat resource yang diperkirakan menggunakan distribusi berbasis biaya ini adalah perkiraan, karena biaya dan emisi diskalakan dengan penggunaan. Meskipun bukan merupakan ukuran yang akurat untuk dampak setiap resource, metode ini membantu memprioritaskan resource yang sering digunakan untuk pengoptimalan.
Gabungkan Set Data: Pertama, gabungkan data biaya penggunaan standar Penagihan Cloud per jam ke tingkat bulanan, yang dikelompokkan menurut ID akun penagihan, project, produk, tag atau label, dan wilayah sebelum bergabung. Kemudian, gabungkan data Jejak Karbon yang diekspor dengan data biaya penggunaan standar Penagihan Cloud menggunakan dimensi umum ID akun penagihan, project, produk, region, dan bulan.
Memperkirakan Emisi Tingkat Tag atau Label: Dengan set data gabungan, gabungkan data emisi menurut tag atau label dan dimensi lainnya sesuai kebutuhan.
Catatan Penting: Emisi tingkat tag atau tingkat label yang digabungkan menggunakan pendekatan ini adalah perkiraan dan mungkin tidak mencerminkan konsumsi energi dan emisi yang sebenarnya secara akurat.
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Sulit dipahami","hardToUnderstand","thumb-down"],["Informasi atau kode contoh salah","incorrectInformationOrSampleCode","thumb-down"],["Informasi/contoh yang saya butuhkan tidak ada","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 2025-08-18 UTC."],[[["\u003cp\u003eLearn how to create a custom dashboard for your Carbon Footprint data using Google Sheets, involving exporting data, using pivot tables, and creating charts.\u003c/p\u003e\n"],["\u003cp\u003eDiscover how to build a custom report in Looker Studio for your Carbon Footprint data by exporting to BigQuery, creating a report, connecting to BigQuery, and adding charts.\u003c/p\u003e\n"],["\u003cp\u003eExplore the process of creating a custom dashboard in Looker for existing Looker customers, which involves exporting data to BigQuery and installing the Carbon Footprint block.\u003c/p\u003e\n"],["\u003cp\u003eYou can further understand the data by understanding the data schema and methodology behind Carbon Footprint.\u003c/p\u003e\n"]]],[],null,["# Create a custom dashboard or analysis for your Carbon Footprint data\n====================================================================\n\nBeyond Carbon Footprint's default view and data export, you can create custom dashboards and analyses using the exported data. This flexibility allows for tailored data visualization and granularity, enabling deeper insights to optimize your carbon footprint management according to your specific requirements.\n\nUsing Google Sheets\n-------------------\n\nFollow these steps to create a custom dashboard for your\nCarbon Footprint data using [Google Sheets](https://www.google.com/sheets/about/).\n\n1. [Export your entire carbon footprint to a sheet](/carbon-footprint/docs/export#sheets)\n2. Use a [pivot table](https://support.google.com/docs/answer/1272900) to create custom reporting on your exported data.\n3. Create a [chart](https://support.google.com/docs/answer/63824) to visualize the results of the pivot table.\n\nUse Looker Studio\n-----------------\n\nFollow these steps to create a custom [Looker Studio](https://lookerstudio.google.com/)\nreport for your Carbon Footprint data.\n\n1. [Export your carbon footprint to BigQuery](/carbon-footprint/docs/export)\n2. [Create a new Looker Studio report](https://support.google.com/looker-studio/answer/6292570)\n3. [Connect to BigQuery](https://support.google.com/looker-studio/answer/6370296) by selecting the dataset you previously chose when configuring the Carbon Footprint export.\n4. [Add charts to your report](https://support.google.com/looker-studio/answer/6293184) using the data source created in the previous step.\n\nUse Looker\n----------\n\nFollow these steps to create a custom [Looker](https://looker.com/)\ndashboard for your Carbon Footprint data.\nThis option is recommended if you are an existing Looker customer.\n\n1. [Export your carbon footprint to BigQuery](/carbon-footprint/docs/export)\n2. Install the [Carbon Footprint block](https://marketplace.looker.com/marketplace/detail/carbon)\n\nEstimate emissions data at different granularity by joining Carbon Footprint data with Cloud Billing data\n---------------------------------------------------------------------------------------------------------\n\nYou can combine your Carbon Footprint export data with your\nCloud Billing export data to view your carbon\nYou can combine your Carbon Footprint export data with your\nCloud Billing export data to view your carbon\nemissions at different levels of granularity. This allows for analysis at\ncustomized label, tag, or resource levels, supporting the identification\nof areas for reducing your environmental impact. You can follow these steps\nto approximate either instance-level emissions data and tag or label level\nemissions data.\n\n### Approximate instance-level emissions data\n\n1. **Export Data to BigQuery** : Export [Carbon Footprint data](/carbon-footprint/docs/export) and [Cloud Billing **detailed** usage cost data](/billing/docs/how-to/export-data-bigquery-tables/detailed-usage) to BigQuery respectively.\n\n2. **Join Datasets** : First, aggregate the hourly Cloud Billing detailed usage cost data to the monthly level, grouped by billing account ID, project, product, resource, and region before joining. Then join exported Carbon Footprint data with [Cloud Billing **detailed** usage cost data](/billing/docs/how-to/export-data-bigquery-tables/detailed-usage) using the common dimensions of billing account ID, project, product, region, and month.\n\n3. **Estimate Resource-Level Emissions**: Break down carbon emissions data (aggregated by billing account ID, project, product, region, and month) to the individual resource level. This estimation can be performed by proportionally distributing the emissions based on each resource's contribution to the cost at list within any given billing account ID, project, product, region, and month. Using cost_at_list avoids side effect of potential pricing discounts. Implement validation steps to identify and mitigate any potential double counting of emissions.\n\n**Important Note**: Resource-level emissions estimated using this cost-based distribution are approximations, as both cost and emissions scale with usage. While not a precise measure of individual resource impact, this method helps prioritize high-usage resources for optimization.\n\n### Approximate tag or label level emissions data\n\n1. **Export Data to BigQuery** : Export [Carbon Footprint data](/carbon-footprint/docs/export) and [Cloud Billing **standard** usage cost data](/billing/docs/how-to/export-data-bigquery-tables/standard-usage) to BigQuery respectively.\n\n2. **Join Datasets** : First, aggregate the hourly Cloud Billing standard usage cost data to the monthly level, grouped by billing account ID, project, product, **tag** or **labels** , and region before joining. Then join exported Carbon Footprint data with [Cloud Billing **standard** usage cost data](/billing/docs/how-to/export-data-bigquery-tables/standard-usage) using the common dimensions of billing account ID, project, product, region, and month.\n\n3. **Estimate Tag-Level or Label-level Emissions** : With the joined dataset, aggregate emissions data by **tag** or **labels** and other dimensions as needed.\n\n**Important Note**: Tag-Level or label-level emissions aggregated using this approach are approximations and may not accurately reflect the actual energy consumption and emissions.\n\nWhat's next?\n------------\n\n- [Read about the data schema used in the export](/carbon-footprint/docs/data-schema).\n- [Understand the methodology behind Carbon Footprint](/carbon-footprint/docs/methodology)"]]
