《價值之鏡：一位SROI實踐者的反思與前行》｜第一篇：良心有價？為「社會效益」貼上價格標籤的思辨之旅

《價值之鏡：一位SROI實踐者的反思與前行》｜第一篇：良心有價？為「社會效益」貼上價格標籤的思辨之旅

Jane Tse, Co-founder, Bigger Life Limited

https://www.linkedin.com/in/jane-tse-biggerlife/

janetse@biggerlife.org

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文章第一篇：良心有價？為「社會效益」貼上價格標籤的思辨之旅

從一堂課說起

上週，我完成了一場關於「社會投資回報率」的深度培訓。作為長期從事可持續發展倡導與教育的顧問，我接觸過不少評估工具，但這次學習讓我持續反思一個根本問題：我們該如何向持份者（投資者、社區、合作夥伴）清晰講述一個項目所創造的、超越金錢的真正價值？

這讓我想起香港社會熱議的維港填海與中環灣仔繞道工程。在類似的重大公共決策中，學者們早已運用成熟的分析工具，嘗試在冰冷的工程預算之外，衡量其對社區、環境、文化遺產的廣泛影響。這些努力，正是為了回答：我們的決策，除了財務回報，究竟為社會創造了多少額外價值？如果能用一套公認的會計方法，證明其社會效益是初始投資的數倍，決策會否更具說服力？

這就是SROI試圖帶來的視角變革。

SROI究竟是什麼？一套「社會價值會計」的系統

SROI，全稱「社會投資回報率」(Social Return on Investment)。用最貼地的話來說，它嘗試為 「社會效益值幾錢？」 這個問題，提供一套理性的分析框架。

它並非憑空出現。早在約20年前，一班來自公益與金融界的先行者意識到，社會價值的討論需要一種更「通用」的商業語言。於是，在2008年左右，致力推廣社會價值理念的社會價值國際網絡應運而生。如今，它已成全球性的實踐社群，在香港亦有活躍的分會。

SROI最特別之處，是將複雜的社會成果（例如：提升健康、減少孤獨感、增強技能）轉化為可比較的貨幣價值。這絕非將「善心」商品化，而是為了在資源有限的現實中，令那些看不見的社會效益與社區福祉，能被「看見」、被量化、被正式納入考量。

水能載舟，亦能覆舟：實踐SROI的「八大核心原則」

任何強大的工具，若使用不當，都可能造成誤導。SROI的創始人深明此理，因此訂立了必須恪守的八大原則，這是整個框架的靈魂所在。

我用更易理解的方式闡釋如下：

1. 邀請持份者參與：切勿閉門造車。項目影響誰，就應邀請誰來定義何謂「價值」。

2. 理解所帶來的改變：全面檢視項目引發的變化，包括預期及非預期的結果。

3. 只對重大成果估值：借用會計學的重要性原則，聚焦關鍵影響，避免糾纏於細枝末節。

4. 只納入已實現的影響：實事求是，不應誇大未來的潛在效益。

5. 避免誇大其詞：保持審慎，對估值採用保守假設。

6. 保持高度透明：公開計算過程與所有假設，讓他人能夠審視檢核。

7. 尋求獨立驗證：邀請第三方核查報告，以增強公信力。

8. 積極回應結果：將評估結論用於學習和改進項目，而非僅作宣傳之用。

小結：價值在於過程，而非單純一個數字

細看這八大原則，前四項核心在於 「以人為本」 ，確保我們量度的是對持份者真正重要的事。後四項則是對設計者與投資者的專業操守要求，強調克制、誠實與透明。

即使最終計算出的「投資回報比」不高，甚至因而決定「不投資」，這個嚴謹的過程本身已極具價值——因為它標誌著，社會價值已正式成為決策天平上不可或缺的砝碼。從這個角度看，SROI的核心貢獻無庸置疑。

然而，將抽象的社會效益量化，真的如此順利嗎？市場上對SROI有哪些褒貶？它在實際操作中最大的挑戰是什麼？我們將在下一篇文章中探討。

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【補充章節】相關概念的發展歷史與影響力

一、核心概念定義

1. 社會價值會計：指衡量、量化和管理社會、環境及經濟影響的系統化框架。它超越了傳統財務會計，試圖捕捉組織活動對所有持份者產生的完整價值。

2. 社會價值衡量：指評估社會干預項目所產生變化的過程與方法學，核心在於回答「我們創造了什麼改變？」。

3. 社會影響力評估：一套更廣泛的流程，用於識別、分析和評估項目對社會的預期與非預期後果。

二、發展歷史里程碑

• 思想萌芽期（1960-80年代）：學界與政府開始不滿足於GDP等經濟指標，嘗試發展衡量社會進步的「社會指標」。

• 方法論奠基期（1990-2000年代）：美國非營利組織REDF首創將社會成果貨幣化以助社會企業融資，社會投資回報率方法由此成形。其後，社會價值國際（SVI）及社會價值英國（Social Value UK）等組織成立，致力推廣及標準化此方法。

• 標準化與主流化期（2010年代至今）：SVI發布《社會價值原則》提供倫理基石。英國2012年《公共服務（社會價值）法案》成為里程碑，要求公共採購須考慮經濟、社會及環境福祉，極大推動了SROI的應用。如今，它更與全球關注的ESG及聯合國可持續發展目標深度融合。

三、影響力分析

1. 對公共政策：推動公共開支決策從「最低價」轉向「最佳整體價值」，令社會效益成為必要考量。

2. 對企業界：助企業量化其社會倡議的非財務價值，用於ESG溝通、風險管理及提升社會牌照。

3. 對非營利與社會企業：提供嚴謹工具向資助方證明成效，並推動內部「循證管理」文化。

4. 對投資領域：催生影響力投資此一資產類別，投資者明確追求可衡量的社會及財務回報。

5. 挑戰：包括方法上如何準確歸因、設定反事實基準，以及貨幣化估值的主觀性。同時存在「影響力漂洗」的風險，需通過獨立核查來制衡。

Fair to bank customers in U.K.: Say NO to biased surveys

Fair to bank customers in U.K.: Say NO to biased surveys

Our UK ESG Watch spotted a regulatory requirement which forces the banks in the U.K. to disclose the quality survey results done by an independent agent in their promotion pamphlets!

The UK's Competition and Markets Authority requires banks to show the survey results to the customers in their promotion materials, this is fair to customers but might be "harsh" to banks. For real-life examples, please see the photos of HSBC UK promotion pamphlets. This requirement applies to both the personal current account providers and the business current account providers.

If you want to know more, please see the survey agent's websites:

Personal banking:

https://www.ipsos.com/en-uk/personal-banking-service-quality

Business banking:

https://www.ipsos.com/en-uk/business-banking-service-quality

Transparency is always a key for enhancing social equity and corporate governance.

Governing Intelligence: Integrating AI Ethics and Data Governance into the UK’s Digital ESG Framework

Governing Intelligence: Integrating AI Ethics and Data Governance into the UK’s Digital ESG Framework

Janet Ng, U.K. ESG Advocate

ymjanetng@gmail.com

In the UK’s rapidly evolving digital economy, artificial intelligence (AI) is transforming business, public administration, and everyday decision‑making. Yet this technological revolution brings not only opportunity, but also profound ethical and governance challenges. Bias, opacity, and misuse of data threaten the very social trust upon which sustainable business depends (House of Lords, 2018; ICO, 2023).

AI governance and data ethics must be integrated into ESG frameworks as core measures of corporate responsibility — not as optional ethics add‑ons. In other words, ethical AI is the new frontier of ESG performance. Only by embedding transparent, fair, and accountable AI systems can the UK create both technological innovation and social legitimacy in the digital age (Leslie, 2019; DSIT, 2024).

Ethical AI Is an ESG Issue

Traditional ESG reporting has focused on carbon footprints, diversity metrics, and board oversight. However, as algorithms increasingly shape how resources, jobs, and information are distributed, AI has become a material governance and social‑impact issue (Leslie, 2019; Floridi, et al., 2018). The rapid growth of AI infrastructure — from energy intensive data centres to the accelerated hardware cycles of machine learning systems — has also considerable environmental consequences (OECD, 2024; DSIT, 2024).

Environment

High computational demand increases carbon emissions, while frequent hardware upgrades contribute to global e‑waste (Floridi, et al., 2018; OECD, 2024). Integrating AI’s environmental footprint into ESG reporting — through indicators such as data‑centre energy intensity, use of renewable energy, and e‑waste recovery — would enable organisations to assess both the ecological and ethical sustainability of their digital operations (CDEI, 2021; DSIT, 2024). In doing so, businesses would complete the E + S + G equation, ensuring technological progress aligns with planetary boundaries as well as human values (Floridi, et al., 2018; Leslie, 2019).

Social

Ethical data is measurable social capital. Transparent data practices sustain public trust and therefore constitute a quantifiable ESG asset (CDEI, 2021; ICO, 2023). If governance is the foundation of ESG, data ethics is its social currency. Organisations now hold unprecedented power over personal and behavioural data. Misuse of that data damages social legitimacy and undermines the “S” of ESG — trust and fairness in stakeholder relations (ICO, 2023; CDEI, 2021). The UK’s Centre for Data Ethics and Innovation (CDEI) defines “trustworthy innovation” as decision‑making that respects privacy, consent, and fairness across the data lifecycle (CDEI, 2021). This transforms data management from an operational task into a moral and social responsibility (Leslie, 2019; Floridi, et al., 2018). Dr Leslie of The Alan Turing Institute (2019) emphasises that responsible data design — using representative, timely, and bias‑checked datasets — is the first ethical barrier against social harm. When companies treat data ethics as integral to ESG, they strengthen social capital: consumer loyalty, employee confidence, and investor assurance (CDEI, 2021; FCA, 2023; Leslie, 2019).

In practice, the UK’s Information Commissioner’s Office (ICO) (2023) requires Data Protection Impact Assessments (DPIAs) for high‑risk AI projects, showing that data transparency is not just good ethics but regulatory ESG compliance. These audits mirror social impact assessments, demonstrating how responsible data governance is now ESG governance (ICO, 2023; DSIT, 2024).

Governance

Responsible AI is a governance imperative. Businesses that ignore AI ethics risk regulatory sanctions, reputational damage, and the erosion of stakeholder trust — core ESG risks (DSIT, 2024; OECD, 2024). The UK’s AI Regulation White Paper (OECD, 2024) affirms that AI must be developed within a governance framework of safety, transparency, fairness, accountability, and contestability — principles that align directly with ESG values. Fairness addresses discrimination; accountability ensures responsible oversight; transparency builds stakeholder trust. These are not only “technical safeguards” — they are corporate governance metrics that investors and regulators can measure and audit (FCA, 2023; DSIT, 2024).

As Dr Leslie (2019) argues, AI ethics must translate values into governance structures. His FAST framework — Fairness, Accountability, Sustainability, and Transparency — maps perfectly onto the S (social) and G (governance) pillars of ESG. Ethical AI, therefore, isn’t just about compliance; it’s about corporate integrity and long‑term sustainability (Leslie, 2019; Floridi, et al., 2018).

Sustainability Through Human‑Centred AI

Ethical AI is social sustainability in action. By ensuring AI systems are transparent, accountable, and inclusive, organisations operationalise the “S” and “G” pillars together (Leslie, 2019; DSIT, 2024). AI’s sustainability implications go beyond energy efficiency. As algorithmic systems begin to shape climate forecasting, healthcare access, and resource allocation, ethical governance becomes central to social sustainability (Floridi, et al., 2018; OECD, 2024). Unchecked bias or discrimination in AI can deepen inequality — contradicting not only ESG ethics but also the UN Sustainable Development Goals (e.g., SDG 16)  (OECD, 2024).

The UK’s AI Report “Ready, Willing and Able?” warns that without transparent oversight, AI risks eroding human dignity and public confidence (House of Lords, 2018). Conversely, when aligned with the Dr Leslie of Alan Turing Institute’s FAST principles and the OECD’s AI recommendations for fairness and accountability (OECD, 2024; Leslie, 2019), AI becomes a force for inclusive, resilient governance. Thus, sustainability in the digital era is not just “green”; it’s ethical and human‑centred. Businesses that ensure AI decisions are explainable, fair, and auditable contribute directly to the longevity and resilience of democratic institutions (Floridi, et al., 2018; CDEI, 2021).

The Public Sector as a Model for Digital ESG

Public‑sector ethical AI frameworks can serve as a governance blueprint for business ESG reporting (Leslie, 2019; FCA, 2023). The UK government and the Alan Turing Institute have established Process‑Based Governance frameworks (PBG) to make algorithmic decisions accountable in public services (Leslie, 2019). These frameworks require documentation such as Dataset Factsheets, Model Cards, and Stakeholder Impact Assessments, ensuring decision traceability. This method mirrors ESG auditing — where organisations record how they identify, mitigate, and disclose risks (FCA, 2023). If extended to the private sector, this “algorithmic audit trails” could form part of future ESG disclosures under the Corporate Sustainability Reporting Directive (CSRD) (DSIT, 2024; CDEI, 2021). By demonstrating accountability‑by‑design, the UK public sector sets a precedent for corporate governance reform. Financial regulators, such as the Financial Conduct Authority (FCA), are already considering integrating AI ethics and data governance metrics into Sustainability Disclosure Requirements (SDR) and investment labels (FCA, 2023).

Conclusion: Toward a Human‑Centred Digital ESG

AI governance and data ethics are the new foundations of ESG integrity — and the measure of whether digital progress in the UK truly serves society (Floridi, et al., 2018; Leslie, 2019). AI’s economic potential will mean little without ethical legitimacy (House of Lords, 2018). As Dr Leslie (2019) puts it, “AI is not morally autonomous; governance must remain a human responsibility (Leslie, 2019).” If ESG once focused on how businesses manage resources and people, the next phase — Digital ESG— will define how they govern intelligence itself. Organisations that embed fairness, transparency, and accountability into digital systems will not only comply with upcoming UK and EU standards, but also secure enduring public trust (DSIT, 2024; FCA, 2023).

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References:

CDEI, 2021. The roadmap to an effective AI assurance ecosystem (Centre for Data Ethics and Innovation). [Online] Available at: https://assets.publishing.service.gov.uk/media/61b0746b8fa8f50379269eb3/The_roadmap_to_an_effective_AI_assurance_ecosystem.pdf [Accessed 15 December 2025].

DSIT, 2024. A pro-innovation approach to AI regulation: government response to consultation (Department for Science, Innovation and Technology). [Online] Available at: https://assets.publishing.service.gov.uk/media/65c1e399c43191000d1a45f4/a-pro-innovation-approach-to-ai-regulation-amended-governement-response-web-ready.pdf [Accessed 18 December 2025].

FCA, 2023. Sustainability Disclosure Requirements (SDR) and investment labels (Financial Conduct Authority). [Online]  Available at: https://www.fca.org.uk/publication/policy/ps23-16.pdf [Accessed 15 December 2025].

Floridi, L. et al., 2018. AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations. [Online] Available at: https://doi.org/10.1007/s11023-018-9482-5 [Accessed 28 December 2025].

House of Lords, 2018. AI in the UK: ready, willing and able?. [Online] Available at: https://publications.parliament.uk/pa/ld201719/ldselect/ldai/100/100.pdf [Accessed 15 December 2025].

ICO, 2023. Guidance on AI and data protection (Information Commissioner's Office). [Online] Available at: https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/artificial-intelligence/guidance-on-ai-and-data-protection/ [Accessed 14 December 2025].

Leslie, D., 2019. Understanding artificial intelligence ethics and safety: A guide for the responsible design and implementation of AI systems in the public sector. [Online] Available at: http://doi.org/10.5281/zenodo.3240529 [Accessed 5 January 2026].

OECD, 2024. Recommendation of the Council on OECD Legal Instruments Artificial Intelligence (Organisation for Economic Co-operation and Development). [Online] Available at: https://legalinstruments.oecd.org/en/instruments/oecd-legal-0449 [Accessed 15 December 2025].

(Date: 9^th January, 2026)