{
  "docId": "019dd923-5de0-76bd-a16b-451df9a6d149",
  "docSlug": "e97464cc0a103dac",
  "documentTitle": "Southeast Asia's Green Economy",
  "authorId": "Bain",
  "authorName": "Bain & Company",
  "documentKindSlug": "consulting-deck",
  "documentKindLabel": "Consulting deck",
  "sourceTypeSlug": "strategy_consulting",
  "sourceTypeLabel": "Strategy consulting",
  "presentationDate": null,
  "orientation": "landscape",
  "aspectRatio": 1.778,
  "pageNumber": 143,
  "pageCount": 174,
  "prevPage": 142,
  "nextPage": 144,
  "slideType": "comparison_table",
  "function": "compare_options",
  "density": "balanced",
  "nDataPoints": 15,
  "notes": "The slide uses a process-flow header to categorize sectors under 'Managing AI growth sustainably' and 'Accelerating AI-driven use cases to drive emissions reduction'.",
  "elementsJson": [
    "headline_text",
    "comparison_table",
    "process_diagram"
  ],
  "metadataConfidence": 1,
  "imagePath": null,
  "slideHref": "/slides/019dd923-5de0-76bd-a16b-451df9a6d149/143",
  "deckHref": "/decks/019dd923-5de0-76bd-a16b-451df9a6d149",
  "deckJsonHref": "/decks/019dd923-5de0-76bd-a16b-451df9a6d149.json",
  "deckAnchorHref": "/decks/019dd923-5de0-76bd-a16b-451df9a6d149#slide-143",
  "components": [
    {
      "bbox": {
        "h": 0.05,
        "w": 0.84,
        "x": 0.12,
        "y": 0.18
      },
      "kind": "diagram",
      "text": "Process flow arrows indicating Managing AI growth sustainably vs Accelerating AI-driven use cases.",
      "attrs": null,
      "subkind": "process",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "88781a63-591d-4621-9415-3d9be79eb3f1",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": null,
      "kind": "metric",
      "text": "ROI range: 15%-50%",
      "attrs": null,
      "subkind": "primary",
      "toolName": "Quantification",
      "toolSlug": "quantification",
      "confidence": null,
      "componentId": "019dd952-0490-77fe-9d3a-ef0343004584",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.02,
        "w": 0.1,
        "x": 0.04,
        "y": 0.96
      },
      "kind": "source-note",
      "text": "Source: Expert interviews",
      "attrs": null,
      "subkind": null,
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "95a17330-a05e-44a6-a4dd-374831573ec5",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.68,
        "w": 0.92,
        "x": 0.04,
        "y": 0.23
      },
      "kind": "table",
      "text": "Table comparing 5 sectors across Project description, SEA market size, ROI range, Risk level, and Key challenges.",
      "attrs": null,
      "subkind": "data",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "181e006e-987c-419d-8128-6565f20311d5",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.08,
        "w": 0.75,
        "x": 0.04,
        "y": 0.06
      },
      "kind": "title",
      "text": "Investments across key green sectors, driven by AI, are expected to deliver a return on investment in the range of 15%-50%",
      "attrs": null,
      "subkind": "action-title",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "d84055a0-1d98-4da8-8f09-126a5103d8be",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [],
  "tools": [],
  "frameworks": [
    {
      "name": "comparison_table",
      "slug": null,
      "matchId": "eb91590d-6987-40e3-867a-f529c93dfb4a",
      "evidence": "Structured grid comparing multiple entities across common attributes.",
      "confidence": 1
    }
  ],
  "arcBeats": [
    {
      "to": 145,
      "from": 59,
      "beatId": "019dd95a-07a5-761b-9144-94c0617c4e8b",
      "arcName": "The Consultant's Gambit",
      "arcSlug": "consultants-gambit",
      "beatName": "Evidence & Proof",
      "beatSlug": "consultants-gambit-evidence-proof",
      "evidence": "Bioeconomy, grid, EV, finance, carbon markets, green AI deep dives with sizing and case studies",
      "position": 4,
      "confidence": 88,
      "parentBeatName": "Evidence",
      "parentBeatSlug": "evidence"
    },
    {
      "to": 151,
      "from": 59,
      "beatId": "019dd95a-07a5-761b-9144-a5b7a27b3bf4",
      "arcName": "The Triple Take",
      "arcSlug": "triple-take",
      "beatName": "The Action (Now What)",
      "beatSlug": "triple-take-the-action-now-what",
      "evidence": "Per-system implementation levers and recommendations",
      "position": 3,
      "confidence": 70,
      "parentBeatName": "Resolution",
      "parentBeatSlug": "resolution"
    }
  ],
  "loops": [
    {
      "to": 145,
      "from": 130,
      "name": "Paradox Resolver",
      "slug": "20-paradox-resolver",
      "bestFor": "Strategic pivots, innovation pitches, challenging conventional wisdom",
      "matchId": "019dd95a-088c-724c-b313-67501415b55e",
      "evidence": "AI/data centers strain power (pp.132-135) but AI use-cases enable 3-5% emission reductions (pp.138-140).",
      "position": 14,
      "objective": "Reconcile AI's energy demand with AI's emissions-reduction upside",
      "structure": "The Apparent Contradiction -> Why Both Seem True -> The Deeper Truth That Reconciles",
      "confidence": 80,
      "description": "Introduce a seeming contradiction that captures attention, then resolve it with a deeper truth"
    }
  ],
  "imagePathAlt": null,
  "thumbSrc": null,
  "thumbSrcAlt": null,
  "locked": true
}