CCDB Factor Search

CCDB碳因子查询与匹配。Find and select the best-fit CCDB carbon / emission factor for PCF, LCA, carbon accounting, ESG, and supply-chain work — not just a raw search result list. Supports 碳因子 / 排放因子 / 碳足迹因子 / emission factor / carbon factor / ecoinvent / ecoinvent factor / ecoinvent 因子 / 因子匹配 / 选因子 / LCA因子 / PCF因子 / scope 3 因子 / BOM因子匹配. Covers a broad range of factor sources across China and international datasets, including ecoinvent-related records and other mainstream public or institutional sources. Also activate proactively when 产品碳足迹计算 / LCA建模 / 碳核算 / 供应链排放测算 clearly need factor data even if the user did not explicitly say "查因子".

Find the best-fit CCDB emission factor for carbon footprint, PCF, LCA, and carbon accounting work — not just a raw result list.

This skill is built for the real business question:

Which factor should I actually use?

It searches in Chinese + English, compares candidates, filters weak matches, explains risks, and returns a recommended factor with direct-use guidance.

It is especially useful when the database is broad and the user needs help choosing from domestic + international sources, including ecoinvent and other mainstream institutional datasets.

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Why this is better than plain search

• plain search → returns a list of possible factors
• this skill → recommends the best-fit factor for the actual use case
• plain search → easy to mis-pick wrong region / wrong unit / wrong factor type
• this skill → filters weak matches and explains risks before use
• plain search → stops at retrieval
• this skill → supports real carbon-accounting decisions

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What this skill does

It can:

• search CCDB factors in Chinese + English
• compare multiple candidates and select the best-fit one
• distinguish carbon footprint factor vs emission factor
• reject weak matches such as wrong-region, wrong-unit, or spend-based factors
• explain whether a result is safe to use directly, review-first, estimate-only, or not suitable
• work across a broad factor data range, including China + international sources and ecoinvent-related records where available in CCDB

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Best-fit use cases

Use this skill when you need to:

• 查碳因子 / 排放因子 / 碳足迹因子
• 因子匹配 / 选因子 / 因子筛选
• LCA 因子 / PCF 因子 / scope 3 因子
• BOM 因子匹配 / 供应商材料因子匹配
• 判断某个因子能不能直接用于正式报告
• 区分 carbon footprint factor vs emission factor
• 避免选错 地区 / 单位 / 因子类型 / 金额口径因子

Also activate proactively when:

• user requests 产品碳足迹建模 / LCA建模 / 排放清单核算 / 情景测算 and factor data is clearly needed
• user provides BOM or material list and asks for carbon footprint calculation
• task involves 供应链碳排放 / scope 3 核算 and activity data is present but no factor has been supplied

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Before vs after

User asks

帮我找中国最新全国电力因子。

Plain search might return

• multiple electricity-related candidates
• mixed carbon-footprint vs emission-factor results
• unclear region / year / direct-use suitability

This skill returns

• one recommended candidate
• why it was selected
• risk notes
• alternatives considered
• whether it is safe for direct use or should be reviewed first

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Very short examples

• 查询最新中国全国电力因子
• 帮我找聚酯切片的碳因子
• 这个因子能不能直接用于正式报告？
• Compare carbon footprint factor vs emission factor for electricity
• Find the best CCDB factor for primary aluminium

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How to invoke

Natural-language examples

• 查询最新中国全国电力因子
• 帮我找聚酯切片的碳因子，如果中文结果不好就切英文继续找
• 这个因子能不能直接用于正式报告？
• Compare carbon footprint factor vs emission factor for electricity
• Find the best CCDB factor for primary aluminium, prefer physical-unit factor

Script examples

bash

python3 scripts/query_ccdb.py --auto --user-request "查询最新的中国全国电力因子，单位最好是 kgCO2e/kWh。"
python3 scripts/query_ccdb.py --query "electricity" --lang en --top 5

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Typical example prompts

Example 1

查询最新的中国全国电力因子，单位最好是 kgCO2e/kWh。

Expected behavior:

• prioritize China electricity candidates
• prefer recent applicable years
• distinguish carbon footprint factor vs emission factor
• return direct-use guidance

Example 2

帮我找聚酯切片的碳因子，如果中文结果不好就切英文继续找。

Expected behavior:

• derive PET / polyester synonyms
• search bilingually
• compare candidates across rounds
• return one recommended factor plus alternatives

Example 3

请帮我找原铝的排放因子，优先物理量单位，不要误选成按金额计算的因子。

Expected behavior:

• reject or downgrade spend-based factors
• prefer physical-unit candidates
• explain why the chosen factor is safer

Example 4

这个因子能不能直接用于正式报告？

Expected behavior:

• explain whether it is direct-use / needs review / estimate-only / not suitable

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Standard output example

yaml

推荐结果:
  匹配等级: close_match
  因子名称: 电力
  因子值: 0.5777
  单位: kgCO2e/kWh
  适用地区: 中国
  适用年份开始: 2024
  适用年份结束: 2024
  发布年份: 2024
  来源机构: 生态环境部
  来源级别: 国家排放因子
  使用建议: 建议人工复核后使用

风险与注意事项:
  - 这是碳足迹因子，不等同于 CO2 排放因子
  - 若用于正式核算或核查，请先确认适用口径

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Key fields to return when possible

A good result should explain these fields clearly:

• 因子名称 / name
• 因子值 / factor value
• 单位 / unit
• 适用地区 / countries
• 适用年份开始 / 结束 / applyYear ~ applyYearEnd
• 发布年份 / year
• 来源机构 / institution
• 来源级别 / sourceLevel
• 来源说明 / source
• 使用建议 / direct-use guidance

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Match classes

• direct_match → highly aligned, usually safe to use after quick sanity check
• close_match → mostly aligned, should usually be reviewed before formal reporting
• fallback_generic → usable only as rough estimate / placeholder
• not_suitable → should not be used directly
• api_unavailable → no recommendation; retry later

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What this skill must do

1. Parse the real search intent

Identify as much as possible from the request:

• material / process / activity
• region
• year
• unit
• use purpose
• whether the user wants 碳足迹因子 or 排放因子

2. Search bilingually

For non-trivial factor matching, do not search in only one language. Always try:

• Chinese core term
• English equivalent
• a few nearby synonyms where needed

3. Rank candidates instead of trusting the first hit

Do not judge a factor from one field only. Key ranking dimensions include:

• semantic fit (name, description, specification)
• region fit (countries)
• unit fit (unit)
• applicability time (applyYear ~ applyYearEnd)
• publication year (year)
• authority (institution, sourceLevel)
• factor-type fit (碳足迹因子 vs 排放因子)

4. Be conservative

Do not force a recommendation when evidence is weak. Prefer:

• not_suitable
• api_unavailable

over a misleading confident answer.

5. Explain the choice

The final answer should explain:

• what was selected
• why it was selected
• what risks remain
• what alternatives were considered
• whether the result can be used directly or only as reference

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Key working rules

Carbon footprint factor vs emission factor

These are not always interchangeable.

• If the user explicitly asks for 碳足迹 / carbon footprint / PCF, prefer carbon footprint factors.
• If the user explicitly asks for 排放因子 / CO2 emission factor / emissions accounting, prefer emission factors.
• If the user only says something vague like “电力因子”, warn that multiple factor types may exist and should not be mixed directly.

China-first bias for Chinese requests

If:

• the request is in Chinese
• no explicit region is given
• the query is geo-sensitive (especially 电力 / 蒸汽 / 天然气)

then Chinese candidates should be preferred by default.

Region warning for geo-sensitive factors

For electricity / steam / natural gas queries, if region is missing, surface that clearly as a risk.

Latest-factor requests

If the user asks for “最新 / latest”, ranking should prefer more recent applyYear, not only lexical similarity.

No spend-based mismatch

If the user wants a physical activity factor, do not recommend spend-based / monetary-unit factors as if they were equivalent.

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Implementation notes

• Main script: scripts/query_ccdb.py
• API contract: references/api-contract.md
• Matching logic notes: references/matching-strategy.md
• Output template: references/output-template.md

If the API contract changes, update the script and references/api-contract.md together.

Keep scoring / filtering logic in code rather than overloading SKILL.md with implementation detail.

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Advanced fallback / debugging

Script unavailable fallback

If scripts/query_ccdb.py is missing or fails to run, fall back to a direct API call:

bash

curl -s -X POST https://gateway.carbonstop.com/management/system/website/searchFactorDataMcp \
  -H 'Content-Type: application/json' \
  -d '{"sign":"<md5(\"mcp_ccdb_search\"+keyword)>","name":"<keyword>","lang":"zh"}'

The sign is md5("mcp_ccdb_search" + keyword). In Python:

python

import hashlib, requests
keyword = "电力"
sign = hashlib.md5(("mcp_ccdb_search" + keyword).encode()).hexdigest()
resp = requests.post("https://gateway.carbonstop.com/management/system/website/searchFactorDataMcp",
    json={"sign": sign, "name": keyword, "lang": "zh"})
print(resp.json())

Even in fallback mode, apply the same ranking, matching, and output rules defined above. Do not return raw API results without analysis.

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Packaging guidance

For public packaging, keep the skill folder lean. Recommended public package contents:

• SKILL.md
• README.md
• _meta.json
• CHANGELOG.md
• scripts/query_ccdb.py
• references/api-contract.md
• references/matching-strategy.md
• references/output-template.md
• evals/evals.json

Draft notes and publishing scratch files should not be included in the final public package.