Forecast of China’s Carbon Emissions Based on ARIMA Method

<div>Fitting results of the moving average series <svg height="11.927pt" id="M102" style="vertical-align:-3.291101pt" version="1.1" viewbox="-0.0498162 -8.6359 10.905 11.927" width="10.905pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M640 650H413L406 622L438 617C475 611 483 605 463 571C422 503 346 400 296 333C264 403 215 529 199 575C189 604 191 614 228 618L262 622L271 650H30L23 622C90 615 97 609 119 553L212 321C222 295 223 285 218 259L193 127C176 39 167 33 82 28L76 0H350L356 28C266 36 257 40 275 127L303 266C308 290 314 302 328 322C396 418 454 493 498 546C551 610 565 616 631 622L640 650Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,7.111,3.132)"><path d="M329 433H203L239 587L230 596L147 534L123 433H57L30 395L34 388H115L61 129C37 16 59 -12 85 -12C147 -12 222 58 260 98L241 125C212 95 160 62 144 62C132 62 127 71 138 126L192 386L305 394L329 433Z"></path></g></svg> of the carbon emission for Guangdong Province.</div>

Discrete Dynamics in Nature and Society

Forecast of China’s Carbon Emissions Based on ARIMA Method

Table 8