Table 9 Optimizing Big Data Policy: a comparison of carbon reduction in cities selected by the original policy and the policy learning model.

DID	\({\hat{\varGamma }}_{i}\)	Policy Learning	\({\hat{\varGamma }}_{i}\)	DID	\({\hat{\varGamma }}_{i}\)	Policy Learning	\({\hat{\varGamma }}_{i}\)
Panel A: GRF
Sanmenxia	−9.426	Sanmenxia	−9.426	Foshan	0.408	Panzhihua	−1.702
Zhoukou	−6.102	Zhoukou	−6.102	Shenzhen	0.466	Sanya	−1.692
Chengde	−5.567	Chengde	−5.567	Zhuhai	0.472	Shantou	−1.688
Xinxiang	−4.930	Xinxiang	−4.930	Shijiazhuang	0.490	Shuangyashan	−1.674
Langfang	−4.119	Langfang	−4.119	Hohhot	0.520	Nanjing	−1.643
Xuchang	−3.090	Xuchang	−3.090	Puyang	0.578	Pingxiang	−1.631
Nanyang	−2.494	Nanyang	−2.494	Pingdingshan	0.749	Luoyang	−1.629
Zhaoqing	−2.329	Zhaoqing	−2.329	Dongguan	0.881	Zhuzhou	−1.602
Jiangmen	−2.075	Jiangmen	−2.075	Shenyang	0.951	Harbin	−1.587
Zhumadian	−2.034	Ezhou	−2.055	Shanghai	1.032	Shuozhou	−1.576
Zhangjiakou	−2.005	Zhumadian	−2.034	Zhongshan	1.135	Datong	−1.561
Luoyang	−1.629	Zhangjiakou	−2.005	Hebi	1.505	Xiamen	−1.554
Xinyang	−1.258	Xiangtan	−1.919	Luohe	1.535	Yichun	−1.536
Shangqiu	−1.081	Yinchuan	−1.753	Qinhuangdao	1.629	Karamay	−1.522
Kaifeng	−0.822	Hezhou	−1.753	Anyang	1.704	Anqing	−1.496
Anshun	−0.815	Jinchang	−1.731	Beijing	1.757	Rizhao	−1.494
Zhengzhou	−0.279	Haikou	−1.725	Tianjin	2.476	Zibo	−1.486
Huizhou	−0.143	Qitaihe	−1.706	Chongqing	3.811	Hangzhou	−1.470
Guangzhou	0.345	Taiyuan	−1.704	Jiaozuo	4.597	Guiyang	−1.468

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