CCDB Factor Search

This skill does not just search factors.
It selects the most usable CCDB factor for real carbon-accounting work, explains why it fits, surfaces risks, and warns when a result should not be used directly.

It is built to answer the real business question:

Which factor should I actually use?

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What this skill does

Use this skill when the user needs more than a raw candidate list.

It can:

• - search CCDB carbon / emission 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 or should only be used as reference

In short:

• - plain factor search → raw factor list
• this skill → best-fit recommendation + risk explanation + use guidance

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When to use this skill

Activate this skill when the user asks to:

• - 查找 / 匹配 / 选择 CCDB 因子
• 查碳因子 / 排放因子 / emission factor / carbon factor
• 比较多个因子候选
• 判断某个因子能不能直接用于正式报告
• 支持 PCF / LCA / 碳核算 / ESG / 供应链核算中的因子匹配

Typical scenarios:

• - product carbon footprint (PCF)
• life cycle assessment (LCA)
• supplier-data factor matching
• carbon accounting / emissions reporting
• sustainability consulting delivery

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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

• - INLINECODE0
• INLINECODE1
• INLINECODE2
• INLINECODE3
• INLINECODE4

Script examples

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

CODEBLOCK1

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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
• INLINECODE6 → mostly aligned, should usually be reviewed before formal reporting
• INLINECODE7 → usable only as rough estimate / placeholder
• INLINECODE8 → should not be used directly
• INLINECODE9 → 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:

• - INLINECODE20
• INLINECODE21

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: INLINECODE23
• API contract: INLINECODE24
• Matching logic notes: INLINECODE25
• Output template: INLINECODE26

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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Packaging guidance

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

• - INLINECODE28
• INLINECODE29
• INLINECODE30
• INLINECODE31
• INLINECODE32
• INLINECODE33
• INLINECODE34
• INLINECODE35
• INLINECODE36

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