Suzhou Shijing Environmental Technology Co (301030) Fair Value & Analysis
Industrials · CN · Market cap 2.4B CNY
Fair value as of: Jun 23, 2026
Analysis
Suzhou Shijing Environmental Technology Co (301030) currently trades at ¥10.00, while our model-based Fair Value estimate is ¥6.09 — implying the stock looks roughly 39.1% overvalued today. We read business quality at 95/100 (high quality), in the Industrials sector. Bear case: priced above our estimate, the market already discounts strong expectations. Bull case: above-average quality can justify a premium — the entry price still matters most (evidence: low).
About the company
Suzhou Shijing Environmental Technology Co.,Ltd. researches, develops, manufactures, and sells pollution detection and environmental protection equipment. The company offers purification, exhaust gas integrated treatment system, photocatalytic system, organic system, silane system, deodorizing organic purification tower, plasma organic exhaust gas purifier, recycling system, catalytic combustion, adsorption desorption, rubber industry vacuum vulcanizer exhaust treatment system, and regenerative thermal incinerator. It also provides dust collection products, such as sintered board dust collector, grinding and cleaning the dust collection system, sand treatment dust collection system, falling sand dust collection system, explosion-proof dust collector, pulsating filter cartridge dust collector, pulsating filter bag dust collector, mine ore processing dust collection series, rolling mill converter dust collecting system, crane passing dust collection system, rotary dust cover, and AOD …
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How we calculate Fair Value
Each company is valued through a stack of independent intrinsic-value models (DCF variants, residual-income, multiples and more), blended into one family-balanced consensus and weighted by how much trustworthy data backs it. A separate quality layer scores the fundamentals. Every input is real reported data — nothing guessed.
Educational research only · not financial advice · no buy/sell recommendation. Model-based estimates are not certainties; their reliability depends on data quality and assumptions.