1.广州市南部水网流域事务中心,广东 广州 510635
2.广东省水利水电科学研究院水资源与水生态环境工程研究所,广东 广州 510635
3.广东省节水技术创新中心,广东 广州 510635
熊艳(1985—),女,高级工程师,主要从事水利水电工程研究。
陈燕珊(1995—),女,硕士,主要从事水文与水资源研究。
收稿:2026-08-20,
修回:2026-09-20,
录用:2026-09-23,
网络首发:2026-10-13,
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熊艳,陈燕珊,邱颂曦等.基于热度指数的广州市节水产业空间分异研究[J].人民珠江,DOI:10.3969/j.issn.1001-9235.XXXX.XX.001.
XIONG Yan,CHEN Yanshan,QIU Songxi,et al.Spatial Differentiation of the Water-Saving Industry in Guangzhou Based on a Development Heat Index[J].PEARL RIVER,
熊艳,陈燕珊,邱颂曦等.基于热度指数的广州市节水产业空间分异研究[J].人民珠江,DOI:10.3969/j.issn.1001-9235.XXXX.XX.001. DOI:
XIONG Yan,CHEN Yanshan,QIU Songxi,et al.Spatial Differentiation of the Water-Saving Industry in Guangzhou Based on a Development Heat Index[J].PEARL RIVER, DOI:10.3969/j.issn.1001-9235...001.
聚焦广义节水产业,以广州市11个行政区为单元,紧扣“需求-供给-成效”全链条逻辑,构建涵盖需求基础、产业活力、节水成效的节水产业发展热度指数体系,采用熵值法、Spearman秩相关和Moran’s I等方法开展量化分析并划分差异化功能区。结果表明,2024年广州市节水产业空间非均衡性显著,总体呈现“东南产业高低凸显、北部涉农区托底、老城需求旺盛但产业薄弱”的分异格局;综合热度指数(含农业)呈显著正向空间集聚(Moran's I为0.570,
<math id="M1"><mi>p</mi><mo>=</mo><mn mathvariant="normal">0.015</mn></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=120038452&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=120038469&type=
13.54666710
2.96333337
),农业节水是驱动空间分异格局中最具区分度的维度;各维度指数耦合协同偏弱,需求基础指数与节水成效指数(含农业)呈显著负相关(
<math id="M2"><mi>r</mi><mo>=</mo><mo>-</mo><mn mathvariant="normal">0.727</mn><mo>
</mo><mi>p</mi><mo>=</mo><mn mathvariant="normal">0.011</mn></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=120038503&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=120038480&type=
29.12533188
3.04800010
),Bootstrap检验确认稳健。据此将11区划分为均衡发展区、转型提升区、农业节水产业培育区、发展滞后区4类功能区,并提出分区发展对策。
Focusing on the water-saving industry in a broad sense
this study takes the 11 administrative districts of Guangzhou as units of analysis. Following the full-chain logic of "demand–supply–effectiveness
" it constructs a Water-Saving Industry Development Heat Index system covering three dimensions: demand foundation
industrial vitality
and water-saving effectiveness. The demand foundation dimension includes per capita GDP
urbanization rate
and the share of the secondary industry; the industrial vitality dimension covers the number of water-saving enterprises
output value
revenue
and R
&
D expenditure; and the water-saving effectiveness dimension includes comprehensive water use per capita
water consumption per 10
000 yuan of GDP
industrial added value per unit of water consump
tion
leakage rate of the urban water supply network
reclaimed water utilization rate
effective utilization coefficient of farmland irrigation water
and the share of area covered by the comprehensive reform of agricultural water pricing. Data are drawn mainly from the Guangzhou Statistical Yearbook (2025)
the Guangzhou Water Resources Report (2025)
project-based enterprise surveys
and district-level self-assessments for water-saving society construction. Quantitative analysis and differentiated functional zoning are conducted using the entropy method
Spearman rank correlation
and Moran's I. To test the robustness of the index results
Monte Carlo perturbation (with ±20% uniform disturbances applied to the baseline weights) and bootstrap resampling are further employed. The results indicate that in 2024
the spatial imbalance of Guangzhou's water-saving industry was pronounced
showing an overall pattern of "prominent industrial highlands in the southeast
agriculture-related districts in the north providing bottom-line support
and strong demand but weak industry in the old urban cores." The comprehensive heat index (including agricultural water-saving indicators) exhibited significant positive spatial clustering (Moran's I 0.570
<math id="M3"><mi>p</mi><mo>=</mo><mn mathvariant="normal">0.015</mn></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=120038453&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=120038470&type=
13.54666710
2.96333337
). After removing agricultural indicators
Moran's I dropped to 0.126(
<math id="M4"><mi>p</mi><mo>=</mo><mn mathvariant="normal">0.619</mn></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=120038504&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=120038481&type=
13.54666710
2.96333337
)
confirming that the observed spatial clustering was mainly attributable to agricultural water-saving effects. The coupling coordination among dimensional indices was generally weak
and the demand foundation index was significantly negatively correlated with the water-saving effectiveness index that included agricultural water-saving indicators(
<math id="M5"><mi>r</mi><mo>=</mo><mo>-</mo><mn mathvariant="normal">0.727</mn><mo>
</mo><mi>p</mi><mo>=</mo><mn mathvariant="normal">0.011</mn></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=120038454&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=120038471&type=
28.36333466
2.96333337
); this result was confirmed by the bootstrap test (95% CI [-0.972
-0.268
]
). This negative association reflects a structural mismatch: highly urbanized districts such as Yuexiu and Huangpu have little agricultural land and thus score low on agricultural water-saving indicators
whereas agricultural districts such as Conghua and Zengcheng achieve high water-saving effectiveness despite weak demand foundations. Meanwhile
the positive trend between demand foundation and industrial vitality did not pass the bootstrap robustness check
suggesting that the linkage between market demand and i
ndustry formation (
<math id="M6"><mi>r</mi><mo>=</mo><mn mathvariant="normal">0.536</mn><mo>
</mo><mi>p</mi><mo>=</mo><mn mathvariant="normal">0.089</mn></math>
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=120038505&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=120038482&type=
26.83933067
2.96333337
)remains statistically inconclusive at the current stage. Accordingly
the 11 districts are divided into four functional zones: the balanced development zone (Panyu
Tianhe
Haizhu)
the transformation and upgrading zone (Huangpu)
the agricultural water-saving industry cultivation zone (Huadu
Conghua
Zengcheng
Baiyun)
and the lagging development zone (Yuexiu
Nansha
Liwan)
with corresponding zonal development strategies proposed. For the balanced development zone
consolidating multi-dimensional synergies and incubating knowledge-intensive water-saving services are recommended. For the transformation and upgrading zone
upgrading high-end water-saving equipment manufacturing and strengthening pipe-network retrofitting is suggested. For the agricultural water-saving industry cultivation zone
integrating agricultural water-saving projects with local industrial chains and expanding precision subsidy mechanisms are advised. For the lagging development zone
introducing industrial carriers
activating the retrofit market through urban renewal
and leveraging green finance tools such as Guangdong's "water-saving loan" program are proposed. Limitations of this study include reliance on 2024 cross-sectional data and the small sample size of 11 districts
which constrain causal inference and long-term trend analysis. Future research could inco
rporate panel data and spatial convergence models to address these gaps.
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