Skip to content

Latest commit

 

History

History
220 lines (157 loc) · 15.8 KB

ImpactScope.md

File metadata and controls

220 lines (157 loc) · 15.8 KB

Greenwasher Identifier

  • Team Name: ImpactScope
  • Payment Address: $address
  • Level: 2

Overview

The Greenwasher Identifier (GWI) project seeks to utilize blockchain and AI to create a transparent, decentralized platform to verify and score the environmental claims of organizations, thus empowering consumers with reliable information. Similarly, GWI can be used internally by corporate entities to continually monitor all types of reporting, social media and company communications to check for potential occurrences of greenwashing before such content is made public.

Project Details

This tool serves as a proactive solution for financial supervisory authorities, asset managers, and companies to save thousands of hours annually while ensuring compliance with evolving regulatory standards.

  • Objective: Develop a decentralized application (dApp) to identify and score corporate greenwashing practices using blockchain and AI.
  • Relevance: Aligns with Web3 Foundation's goals by promoting transparency, decentralization, and leveraging blockchain technology to address societal challenges.
  • Technology Stack:
    • Blockchain for Transparency: Utilize a blockchain to record and verify environmental claims, ensuring immutability and transparency.
    • AI for Analysis: Implement AI algorithms to evaluate the authenticity of companies' environmental efforts and assign scores.
  • Core Components: AI modules for qualitative and quantitative analysis and blockchain integration for preserving records on the IPFS, linked through individual hash addresses on the Astar chain.
  • User-Friendly Dashboard: A comprehensive dashboard provides stakeholders with real-time insights, analytics, and access to reports, enhancing decision-making based on accurate and verified metrics.
  • Comprehensive Reporting: Generates detailed reports highlighting discrepancies, unsubstantiated claims, and potential inconsistencies in environmental disclosures.

Example Reports

Demo reports page with a few examples of the reports generated by the Greenwasher Identifier.

Example Report


EasyJet's Environmental Disclosure Analysis

Analysis Date: Mar 1, 2024, 12:19:23 PM GMT+1

EasyJet's Environmental Analysis

Key Findings:

  1. Identification of non-material treatment of water, biodiversity, and emissions contrary to claims of sustainability leadership
  2. Contradictions in renewable energy commitments vs. the absence of specific emissions reduction targets.
  3. Unlikely achievement of net zero emissions by 2050 based on existing scientific consensus.
  4. Obscured overall carbon footprint due to non-disclosure of non-UK sites emissions.
  5. General lack of substantiated data to support the effectiveness of reported sustainability initiatives.

AIB Group's Environmental Disclosure Analysis

Analysis Date: Mar 1, 2024, 04:04:27 PM GMT+5

AIB Group's Environmental Analysis

Key Findings:

  1. Discrepancies in lending and "net zero" pledges indicating differing ambitions.
  2. Increased Scope 3 emissions without adequate context or justification.
  3. Inconsistencies and lack of clarity in green lending figures and GHG emissions reductions.
  4. Unsubstantiated claims about the encouragement of suppliers' carbon emissions reporting and ambiguous alignment with SDG 13 'Climate Action'.
  5. Promotions of lower-carbon business models in sectors with delayed net zero targets, raising questions about the alignment with overall net zero goals.

Ecosystem Fit

  • Integration into Ecosystem: The GWI fits into the ecosystem as a unique tool leveraging both AI and blockchain technologies to monitor, verify, and report on environmental claims, enhancing transparency and accountability in corporate sustainability efforts.
  • Target Audience: Financial regulatory bodies, asset managers, and corporations focusing on sustainable practices.
  • Needs Addressed: The demand for accurate monitoring and reporting on green practices to combat greenwashing.
  • Unique Aspect: Unlike any existing projects within the ecosystem, GWI uniquely combines AI's analytical power with blockchain's immutability to create a comprehensive greenwashing detection system.

Team 👥

Team members

  • Name of team leader
  • Names of team members

Contact

  • Contact Name: Full name of the contact person in your team
  • Contact Email: Contact email (e.g. [email protected])
  • Website: Your website

Legal Structure

  • Registered Address: Address of your registered legal entity, if available. Please keep it in a single line. (e.g. High Street 1, London LK1 234, UK)
  • Registered Legal Entity: Name of your registered legal entity, if available. (e.g. Duo Ltd.)

Team's experience

ImpactScope is a sustainability focused technology company building AI and web3 tools for social enterprises, financial regulators, NGOs and digital asset ventures. We have a corporate presence in Switzerland and Estonia and team members in seven different countries. Our team is a unique blend of impact entrepreneurs, data scientists, researchers, software engineers and sustainability practitioners, including recognized global authorities in behavioral economics, tokenomics engineering, NLP and smart contract design. Awards and Recognition

ImpactScope is parthering with Hashed Systems DAO LLC, a substrate development team with years of experience building blockchain applications. They have worked on substrate and Polkadot since spring 2021. Their developers completed Brian Chen's course and have experience running substrate chains and have significant experience working with the Uniques, Identity and Node-authorization pallets. Additional relevant experience below:

Hypha DAO: Smart contracts and front end development that enables the creation of flexible roles, assignments and contributions with recurring payments. Design and implement a graph data layer to improve web application performance. Design and build a Double Entry accounting (Passcode: .V$C#Br2) plattform that streams wallet activity, supports token price history, reporting and currency conversion.

SEED: Smart contract and mobile development that capture the project's constitution, enable voting on proposals and basic identity management like reputation, vote history etc. Design and build a PWA token swaps app. Design and build a basic Economic Simulator that enables voters to understand the economic impact of policy changes.

Team Code Repos

Please also provide the GitHub accounts of all team members. If they contain no activity, references to projects hosted elsewhere or live are also fine.

Team LinkedIn Profiles (if available)

Development Status 📖

Our project has been rigorously tested and proven, initially employing AI models integrated with the Ethereum Sepolia testnet for greenwashing detection. The current phase of our work is centered around transitioning our underlying blockchain infrastructure to the Astar network. This migration aims to leverage the Astar network's features, such as enhanced scalability, interoperability, and smart contract functionality, to optimize the operational efficiency and impact of the Greenwasher Identifier.

Development Roadmap 🔩

Overview

  • Total Estimated Duration: 15 weeks
  • Full-Time Equivalent (FTE): 3 FTE (across 6 developers)
  • Total Costs: 78,200 USD

Milestone 1 — Migration Design and Architecture Planning

  • Estimated duration: 2 week
  • FTE: 3
  • Costs: 6,450 USD (6 contributors)
Number Deliverable Specification
0a. License MIT
0b. Documentation We will provide inline documentation of the code and a basic tutorial of the modules delivered in this Milestone.
0c. Testing Unit testing will be applied to ensure reliability. Documentation of tests and results will be provided
0d. Video We will provide a video demonstration that will illustrate all of the functionality delivered with this milestone.
0e. Article We will publish an article in English and Spanish that explains what was built and how it can benefit other projects
1. Documentation Comprehensive documentation outlining the planned migration architecture, including system diagrams and component interactions tailored to the Astar network.
2. Architectural Review A detailed session for architectural review with stakeholders for feedback and final approval of the migration design.

Milestone 2 — Migration and Development

  • Estimated duration: 4 weeks
  • FTE: 3
  • Costs: 23,580 USD (6 contributors)
Number Deliverable Specification
0a. License MIT
0b. Documentation We will provide inline documentation of the code and a basic tutorial of the modules delivered in this Milestone.
0c. Testing Unit testing will be applied to ensure reliability. Documentation of tests and results will be provided
0d. Video We will provide a video demonstration that will illustrate all of the functionality delivered with this milestone.
0e. Article We will publish an article in English and Spanish that explains what was built and how it can benefit other projects
1. Migration Execution Complete migration of the existing system from the Ethereum Sepolia testnet to the Astar network.
2. Smart Contracts Development and deployment of smart contracts on the Astar network to manage and verify greenwashing reports.
3. AI Integration Integration of AI analysis with the blockchain on the Astar network for automatic recording and verification of greenwashing instances.
4. Data Structure Update Update the system to support the data structure and storage in the Astar ecosystem, including IPFS integration for permanent recordkeeping.
5. Interface Design Begin preliminary interface design adjustments for Astar compatibility.
6. Docker Container We will provide a Docker container that encapsulates the entire software stack for easy deployment by end-users.
7 Migration Report A report outlining the migration strategy, challenges encountered, and solutions applied, aimed at aiding similar migration efforts.

Milestone 3 — Testing and Deployment

  • Estimated duration: 4 weeks
  • FTE: 3
  • Costs: 23,580 USD (6 contributors)
Number Deliverable Specification
0a. License MIT
0b. Documentation We will provide inline documentation of the code and a basic tutorial of the modules delivered in this Milestone.
0c. Testing Unit testing will be applied to ensure reliability. Documentation of tests and results will be provided
0d. Video We will provide a video demonstration that will illustrate all of the functionality delivered with this milestone.
0e. Article We will publish an article in English and Spanish that explains what was built and how it can benefit other projects
0e. Deployment Toolkit A toolkit comprising scripts and utilities for straightforward deployment, configuration, and management of the system.
1. Testing Conduct extensive testing on Astar network functionality, ensuring compatibility and performance optimization.
2. Interface Enhancements Finalize user interface enhancements to provide an intuitive experience for users operating within the Astar ecosystem.
3. Deployment Deploy the Greenwasher Identifier on the Astar network, ensuring robust security measures are in place.
4. Documentation Prepare comprehensive documentation for users and developers, detailing system operation and smart contract interactions.

Milestone 4 — Interface Improvement and User Testing

  • Estimated duration: 3 weeks
  • FTE: 3
  • Costs: 13,590 USD (6 contributors)
Number Deliverable Specification
0a. License MIT
0b. Documentation We will provide inline documentation of the code and a basic tutorial of the modules delivered in this Milestone.
0c. Testing Unit testing will be applied to ensure reliability. Documentation of tests and results will be provided
0d. Video We will provide a video demonstration that will illustrate all of the functionality delivered with this milestone.
0e. Article We will publish an article in English and Spanish that explains what was built and how it can benefit other projects
1. UI/UX Design Refinement of the user interface to enhance usability and visual appeal, ensuring a seamless experience for stakeholders.
2. Report Detailed report on user testing sessions, including feedback, adjustments made, and final usability testing results.
3. UI/UX Design Files The finalized UI/UX design files and style guides.

Milestone 5 — Outreach and Initial Partnerships

  • Estimated duration: 2 week
  • FTE: 3
  • Costs: 11,000 USD (6 contributors)
Number Deliverable Specification
0a. License MIT
0b. Documentation We will provide inline documentation of the code and a basic tutorial of the modules delivered in this Milestone.
0c. Testing Unit testing will be applied to ensure reliability. Documentation of tests and results will be provided
0d. Video We will provide a video demonstration that will illustrate all of the functionality delivered with this milestone.
0e. Article We will publish an article in English and Spanish that explains what was built and how it can benefit other projects
1. Outreach Plan Initiate outreach activities to financial regulators, asset managers, and corporations to promote the adoption of the Greenwasher Identifier.
2. Partnerships Forge strategic partnerships with sustainability-focused organizations and blockchain entities to enhance the project's reach and efficacy.
3. Educational Content Develop educational content, including workshops, webinars, and presentations, to educate stakeholders on the benefits of leveraging blockchain technology for sustainability reporting and greenwashing detection.
4. Feedback Analysis Continuously monitor feedback from users and partners to iterate and improve the Greenwasher Identifier, ensuring it remains at the forefront of combating greenwashing.

Future Plans

As we look beyond the initial development and deployment phases of the Greenwasher Identifier (GWI) on the Astar network, our vision is to expand and publicly document compliance in other key areas. Here are our key objectives for the future:

Expanding Technological Capabilities

  • Advanced AI Models: Continuously refine and enhance our AI algorithms, incorporating more sophisticated natural language processing techniques and machine learning models to improve the accuracy and efficiency of greenwashing detection.