---
title: Data Resource Impact on Green Growth
url: https://www.ml-quant.com/papers/ssrn/4935262/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 4935262
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4935262
featured: 2024-08-28
citations: unknown
topic: ML & AI Methods
---


# Data Resource Impact on Green Growth

A study using Double Machine Learning methods on Chinese cities from 2000 to 2021 finds that a 1% increase in data resources correlates with a 2.1% rise in inclusive green growth, driven mainly by talent.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4935262
- Identifier: SSRN 4935262
- Released: 2024-08-23
- First featured: Quant Letter No. 63 (2024-08-28): https://www.ml-quant.com/issues/2024-08-28/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: ML & AI Methods

## Related

- [Model Averaging and Double Machine Learning](https://www.ml-quant.com/papers/ssrn/4691169/): The article presents two new stacking methods for double-debiased machine learning (DDML), showing its robustness against unknown functional forms, with software available in Stata and R.
- [Vaccination Impact on Mortality](https://www.ml-quant.com/papers/repec/oup-emjrnl-v-27-y-2024-i-2-p-299-322/): The paper uses double machine learning to estimate the impact of vaccination on COVID-19 mortality in the EU, finding that a 10% increase in doses significantly reduces deaths and that Moderna and AstraZeneca vaccines are more cost-effective than Pfizer.
- [Guidelines for Double/Debiased ML in Economics](https://www.ml-quant.com/papers/ssrn/4703243/): The article discusses the integration of machine learning in economics, focusing on the DoubleDebiased Machine Learning framework and the importance of model generalizability.
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://www.ml-quant.com/papers/arxiv/2312.00752/): Sequence Modeling: Mamba, a neural network architecture that doesn't use attention or MLP blocks, provides faster inference and better performance in language, audio, and genomics than Transformers.
- [Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality](https://www.ml-quant.com/papers/arxiv/2405.21060/): The research identifies a link between state-space models and Transformers in deep learning, leading to the creation of a faster language modeling architecture, Mamba-2.
- [Octo: An Open-Source Generalist Robot Policy](https://www.ml-quant.com/papers/arxiv/2405.12213/): Octo is a large transformer-based policy for robotic manipulation, trained on a vast dataset, that can be instructed via language or images and adapted to new domains.
