Table 4 Performance validation of the correlation model in different cities.

From: Dynamic correlation analysis of sectoral electricity consumption and urban carbon concentration using machine learning models

City

Random Forest

XGBoost

Proposed Correlation Model

R2

RMSE

R2

RMSE

R2

RMSE

1

0.8071

1.608

0.862

1.3603

0.864

1.3502

2

0.8096

1.699

0.7975

1.7521

0.8528

1.4939

3

0.7084

1.991

0.8165

1.5794

0.8327

1.5082

4

0.7314

2.0335

0.8068

1.7246

0.8325

1.6058

5

0.7587

2.0009

0.7817

1.9029

0.7846

1.8904

6

0.6775

2.209

0.7723

1.8561

0.8301

1.6032

7

0.7328

2.0402

0.7283

2.0573

0.7389

2.0167

8

0.7028

2.1095

0.7389

1.9774

0.7553

1.9142

9

0.8109

1.6436

0.7352

1.9446

0.8539

1.4447

10

0.7266

1.9931

0.7331

1.9695

0.7745

1.8103

11

0.5741

2.4638

0.6907

2.0996

0.732

1.9544

12

0.657

2.2011

0.7238

1.975

0.755

1.86

13

0.733

1.9413

0.7166

1.9999

0.7465

1.8913

14

0.5974

2.578

0.694

2.2474

0.7613

1.9849

15

0.7179

2.0592

0.6358

2.3397

0.7754

1.8372

16

0.6323

2.3801

0.6601

2.2882

0.6991

2.153

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