Regional Sustainability ›› 2023, Vol. 4 ›› Issue (3): 261-281.doi: 10.1016/j.regsus.2023.08.002cstr: 32279.14.j.regsus.2023.08.002
• Full Length Article • Previous Articles Next Articles
Fabiana MANSERVISIa,*(), Michele BANZIa, Tomaso TONELLIa, Paolo VERONESIa, Susanna RICCIa, Damiano DISTANTEb, Stefano FARALLIc, Giuseppe BORTONEa
Received:
2022-12-13
Revised:
2023-06-09
Accepted:
2023-08-18
Published:
2023-09-30
Online:
2023-10-20
Contact:
*E-mail address: Fabiana MANSERVISI, Michele BANZI, Tomaso TONELLI, Paolo VERONESI, Susanna RICCI, Damiano DISTANTE, Stefano FARALLI, Giuseppe BORTONE. Environmental complaint insights through text mining based on the driver, pressure, state, impact, and response (DPSIR) framework: Evidence from an Italian environmental agency[J]. Regional Sustainability, 2023, 4(3): 261-281.
Table 2
Example of complaint records from the online claim submission system “Contact Arpae”."
Field | Content |
---|---|
Date | 15:04:15 on 24 June 2021 |
Complaint ID | 64890 |
Province | Modena |
Municipality | Maranello |
Personal contact information | XXXX |
User classification | Private |
Claim topic | Odor |
Claim content | I cannot open the window because the nearby factory smells like burning rubber at nighttime |
Public Relations Office (Italian acronym for “Ufficio Relazioni con il Pubblico (URP) response | Your request has been taken care of by our territorial service and you will be contacted as soon as possible |
Response date | 09:10:30 on 25 June 2021 |
Table 3
Stakeholders’ claim topic on environmental complaints."
Claim topic | Percentage of claim topic to the total environmental complaints (%) | |||
---|---|---|---|---|
Public | Companies | Local authorities | Citizen associations | |
Air pollution | 32.8 | 11.3 | 17.2 | 25.0 |
Water pollution | 17.5 | 28.9 | 21.9 | 10.0 |
Noise pollution | 16.8 | 2.6 | 34.4 | 20.0 |
Waste | 8.7 | 10.3 | 6.2 | 7.5 |
Soil | 7.4 | 4.1 | 3.9 | 2.5 |
Generic information | 5.4 | 10.3 | 2.3 | 10.0 |
Electromagnetic radiation | 4.3 | 6.2 | 12.5 | 10.0 |
Odor | 4.0 | 1.5 | 1.6 | 10.0 |
Weather-climate | 2.1 | 12.4 | - | 5.0 |
Environmental authorization | 0.8 | 11.9 | - | - |
Sea-coast | 0.2 | 0.5 | - | - |
Total | 100.0 | 100.0 | 100.0 | 100.0 |
Table 4
Regional distribution of environmental complaints raised by the public."
Complaint topic | Number of environmental complaints | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Bologna | Ferrara | Forlì-Cesena | Modena | Parma | Piacenza | Ravenna | Reggio-Emilia | Rimini | Total | |
Air pollution | 75 | 69 | 41 | 216 | 57 | 60 | 68 | 57 | 22 | 665 |
Water pollution | 51 | 21 | 35 | 57 | 69 | 29 | 38 | 43 | 11 | 354 |
Noise pollution | 69 | 27 | 27 | 32 | 55 | 41 | 27 | 35 | 26 | 339 |
Waste | 18 | 9 | 9 | 23 | 46 | 22 | 26 | 17 | 6 | 176 |
Soil | 8 | 5 | 9 | 42 | 50 | 19 | 7 | 10 | - | 150 |
Odor | 32 | 6 | 5 | 6 | 10 | 3 | 2 | 6 | 12 | 82 |
Generic information | 10 | 6 | 11 | 32 | 17 | 4 | 9 | 17 | 3 | 109 |
Electromagnetic radiation | 18 | 10 | 5 | 12 | 14 | 4 | 9 | 10 | 5 | 87 |
Weather-climate | 13 | 1 | 4 | 11 | 2 | 3 | 4 | 3 | - | 41 |
Environmental authorization | 3 | 1 | 2 | 1 | 1 | 2 | 3 | 3 | - | 16 |
Sea-coast | - | - | 2 | - | - | 1 | - | - | - | 3 |
Total | 297 | 155 | 150 | 432 | 321 | 188 | 193 | 201 | 85 | 2022 |
Table 5
Regional distribution of environmental complaints raised by the companies."
Complaint topic | Number of environmental complaints | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Bologna | Ferrara | Forlì-Cesena | Modena | Parma | Piacenza | Ravenna | Reggio-Emilia | Rimini | Total | |
Water pollution | 10 | 6 | 3 | 4 | 7 | 5 | 12 | 6 | 1 | 56 |
Weather-climate | 6 | 4 | - | 1 | 1 | 5 | 3 | 4 | - | 24 |
Environmental authorization | 7 | 2 | 3 | 4 | 2 | 2 | 1 | 1 | 1 | 23 |
Air pollution | 5 | 1 | 1 | 5 | - | 2 | 2 | 5 | 1 | 22 |
Generic information | 5 | 1 | 3 | 3 | 2 | 1 | 2 | 1 | 2 | 20 |
Waste | 3 | 1 | 2 | - | 5 | 1 | 3 | 2 | 3 | 20 |
Electromagnetic radiation | - | - | - | - | 1 | 1 | 1 | 1 | 8 | 12 |
Soil | 1 | - | - | 2 | 4 | 1 | 1 | - | - | 8 |
Noise pollution | 1 | - | - | 2 | - | - | - | 1 | 1 | 5 |
Odor | - | - | - | 3 | - | - | - | - | - | 3 |
Sea-coast | 1 | - | - | - | - | - | - | - | - | 1 |
Total | 39 | 15 | 12 | 24 | 22 | 18 | 25 | 21 | 19 | 194 |
Table 6
Regional distribution of environmental complaints raised by the local authorities."
Complaint topic | Number of environmental complaints | ||||||||
---|---|---|---|---|---|---|---|---|---|
Bologna | Ferrara | Forlì-Cesena | Modena | Parma | Piacenza | Ravenna | Rimini | Total | |
Noise pollution | 1 | - | 8 | 1 | 1 | 1 | 8 | 24 | 44 |
Water pollution | 6 | 1 | 10 | 2 | 1 | 1 | 2 | 5 | 28 |
Air pollution | 1 | 2 | 2 | - | 1 | 1 | 3 | 12 | 22 |
Electromagnetic radiation | 1 | - | 5 | - | - | - | 1 | 9 | 16 |
Waste | 1 | - | 3 | 1 | - | 3 | - | 8 | |
Soil | - | - | 2 | - | 2 | - | 1 | - | 5 |
Generic information | 1 | - | 1 | - | - | - | - | 1 | 3 |
Odor | 1 | 1 | - | - | - | - | - | 2 | |
Total | 11 | 4 | 32 | 4 | 5 | 3 | 18 | 51 | 128 |
Table 7
Regional distribution of environmental complaints raised by the local authorities."
Complaint topic | Number of environmental complaints | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Bologna | Ferrara | Forlì-Cesena | Modena | Parma | Piacenza | Ravenna | Reggio-Emilia | Rimini | Total | |
Air pollution | - | 2 | 1 | 1 | 5 | - | 1 | - | - | 10 |
Noise pollution | 2 | - | 2 | 1 | 2 | - | - | - | 1 | 8 |
Electromagnetic radiation | 2 | - | 1 | - | - | - | - | - | 1 | 4 |
Generic information | 1 | 2 | - | - | - | 1 | - | - | - | 4 |
Odor | - | - | 3 | - | - | 1 | - | 4 | ||
Water pollution | 3 | - | 1 | - | - | - | - | - | 4 | |
Waste | 1 | - | - | - | - | 2 | - | - | - | 3 |
Weather-climate | 1 | - | 1 | - | - | - | - | - | - | 2 |
Soil | - | - | - | - | - | - | - | 1 | - | 1 |
Total | 10 | 4 | 6 | 5 | 7 | 3 | 1 | 2 | 2 | 40 |
Table 9
Keywords and TF-IDF scores of air pollution and odor in the DPSIR framework."
Category | Sub-category | Keywords (TF-IDF scores) |
---|---|---|
Driver | Economic driving forces | Factory (0.018), foundry cooperatives (0.017), farm (0.010), installation (0.006), fireplace (0.005), ceramics (0.004), yard (0.003), dairy farm (0.003), incinerator (0.002), biogas plant (0.002), and painting (0.001) |
Pressure | Environmental pressures | Smoke (0.019), fumes (0.009), emissions (0.007), dusts (0.007), wind (0.006), pollution (0.004), gas fuels (0.004), hydrocarbons (0.003), sludge (0.003), and poultry manure (0.002) |
Human behavior pressures | Burning (0.005), spreading slurries (0.004), spreading manure (0.004), burning waste (0.004), trucks traffic (0.003), and spillage (0.001) | |
State | Environmental state (abiotic state-environment) | Burnt plastic (0.019), acrid smell (0.014), burning smell (0.012), unbreathable air (0.011), smelly fumes (0.009), bitumen smell (0.008), chemical smell (0.007), burnt rubber (0.005), fire (0.004), ammonia smell (0.003), smell of paint (0.003), acidic smell (0.002), and smog (0.001) |
Environmental state (biotic state-inhabitants) | Mosquitoes (0.003) | |
Impact | Human well-being (health and safety) | Strong smell (0.029), hours (0.017), stink (0.010), windows closed (0.007), unbearable odor (0.007), sore throat (0.006), unpleasant smell (0.006), nauseating smell (0.005), health (0.004), discomfort (0.003), headache (0.003), respiratory problems (0.002), eye irritation (0.002), and awakening (0.001) |
Response | Driving forces-based responses | Request (0.006), check (0.004), intervention (0.003), inspection (0.003), data (0.003), mail feedback (0.002), police (0.002), controls (0.002), and compliance (0.001) |
Impact-based responses | Monitoring (0.001) |
Table 10
Keywords and TF-IDF scores of water pollution in the DPSIR framework."
Category | Sub-category | Keywords (TF-IDF scores) |
---|---|---|
Driver | Economic driving forces | Factory (0.007) and farm (0.004) |
Pressure | Environmental pressures | Foam (0.023), wastewater discharge (0.017), drainage system (0.010), pollution (0.010), well (0.008), hydrocarbons (0.006), discharge in sewer (0.005), fumes (0.003), and detergent (0.002) |
Human behavior pressures | Spillage (0.020), spreading slurries (0.011), gas oil spilling (0.005), pipe burst (0.003), and accident (0.002) | |
State | Environmental state (abiotic state-living habitat) | Canal (0.035), stream (0.020), and river (0.012) |
Environmental state (abiotic state-environment) | Water discoloration (0.011), algae (0.002), and environmental degradation (0.002) | |
Environmental state (biotic state-inhabitants) | Fish (0.014) | |
Impact | Human well-being (health and safety) | Bad smell (0.015), smelly (0.011), strong smell (0.005), and nauseating smell (0.004). |
Response | Driving forces-based responses | Request (0.013), police (0.011), check (0.007), inspection (0.008), intervention (0.004), data (0.004) voluntary ecological guards (0.003), and information (0.003) |
Pressure-based responses | Waste management (0.004) and water treatment plant (0.003) | |
Impact-based responses | Analyses (0.002) | |
State-based responses | Remediation (0.010) |
Table 11
Keywords and TF-IDF scores of waste in the DPSIR framework."
Category | Sub-category | Keywords (TF-IDF scores) |
---|---|---|
Driver | General driving force | Landfill (0.009) |
Pressure | Environmental pressures (discharge) | Littering (0.026), asbestos (0.024), material (0.017), plastic (0.010), demolition waste (0.009), liquid waste (0.005), and oil in the soil (0.005) |
Human behavior pressures | Spillage (0.008), illegal dump (0.008), truck transport (0.007), incident (0.004), abandoned cars (0.004), waste burning (0.004), and abandoned rubbers (0.003) | |
State | Environmental state (abiotic state-environment) | Fire (0.016), environmental degradation (0.006), and contaminated soil (0.002) |
Environmental state (biotic state-living habitat) | Soil (0.014), river (0.010), and vegetation (0.004) | |
Impact | Human well-being (health and safety) | Danger (0.006), odor (0.004), and bad smell (0.003) |
Response | Driving forces-based responses | Request (0.016), action (0.013), inspection (0.011), police (0.010), information (0.004), feedback (0.004), data (0.003), and waste disposal (0.009) |
Pressure-based responses | Uncontrolled disposal (0.007), waste recovery (0.005), waste transport (0.005), and waste management (0.005) |
Table 12
Keywords and TF-IDF scores of noise pollution in the DPSIR framework."
Category | Sub-category | Keywords (TF-IDF scores) |
---|---|---|
Driver | Economic driving forces | Factory (0.015), bar (0.010), installations (0.010), hotel (0.008), restaurant (0.007), yard (0.006), supermarket (0.005), traffic (0.004), air conditioning system (0.004), irrigation system (0.004), train (0.003), motorway (0.003), and filtering system (0.002) |
Social driving forces | Barking of dogs (0.007), music (0.007), and bells (0.004) | |
Pressure | Environmental pressures | Vibrations (0.004) and emissions (0.004) |
State | Environmental state (abiotic state physical and chemical) | Loud noise (0.006) and decibel (0.003) |
Impact | Human well-being (health and safety) | Trouble (0.014), condominium (0.012), house (0.012), night disturbances (0.009), all day long disturbances (0.009), disturbing noise (0.006), continuous noise (0.005), not sleeping (0.005), loud noises (0.005), discomfort (0.004), windows closed (0.004), unbearable noise (0.003), and rest (0.003) |
Response | Driving forces-based responses | Request (0.038), check (0.015), verification (0.009), inspection (0.009), information (0.009), intervention (0.008), and investigations (0.007) |
Impact-based responses | Quiet (0.005) and surveys (0.005) |
Table 13
Keywords and associated TF-IDF scores of electromagnetic radiation in the DPSIR framework."
Category | Sub-category | Keywords (TF-IDF scores) |
---|---|---|
Driver | Economic driving forces (culture and infrastructure) | Radiobase stations (0.027), mobile phone (0.022), antenna (0.019), cell tower (0.019), cab (0.017), power line (0.009), radiobase stations (0.027), mobile phone (0.022), antenna (0.019), cell tower (0.019), and cab (0.017) |
Pressure | Environmental pressures | Pollution (0.021), radio-frequencies (0.016), emissions (0.015), field (0.012), and waves (0.012) |
State | Environmental state (abiotic state-physical and chemical) | High voltage (0.009) |
Impact | Human well-being (health and safety) | Health (0.011), exposure (0.009), risks (0.005), and concern (0.002) |
Response | Driving forces-based responses | Request (0.051), information (0.015), check (0.013), data (0.011), feedback (0.005), thematic maps (0.004), and appointment (0.002) |
Pressure-based responses | Monitoring (0.024), measurement (0.023), meters (0.008), distance (0.006), nearby (0.005), survey (0.006), and values (0.005) |
Table 14
Keywords and associated TF-IDF scores of soil in the DPSIR framework."
Category | Sub-category | Keywords (TF-IDF scores) |
---|---|---|
Driver | Economic driving forces | Farm (0.024), cattle farm (0.010), factory (0.009), stable (0.005), and pig farm (0.005) |
Pressure | Human behavior pressures | Spreading slurries (0.044), spreading manure (0.027), spillage (0.017), dung heap (0.013), land spreading (0.011), tank (gas oil spillage) (0.010), burial (gas oil tank) (0.009), storage (0.009), oil spillage (0.007), substances (spillage) (0.005), jet of manure (0.005), and surface impoundment (0.004) |
Environmental pressures | Zootechnical effluents (0.018), sludge (0.011), gas oil (0.009), pollution (0.007), and fumes (0.004) | |
State | Environmental state (biotic state-living habitat) | Water contamination (by manure) (0.008), and stream contamination (by manure) (0.008) |
Environmental state (abiotic state-environment) | Fire (0.007) and paludification (0.005) | |
Environmental state (biotic state-inhabitants) | Mosquitoes (0.011) | |
Impact | Human well-being (health and safety) | Odor (0.018), unpleasant smell (0.006), nauseating smell (0.006), strong smell (0.005) and discomfort (0.005) |
Response | Driving forces-based responses | Request (0.016), police (0.016), control (0.011), intervention (0.011), and check (0.009) |
Pressure-based responses | Management (0.006) and compliance (distance) (0.006) | |
Impact-based responses | Stop (0.005) |
Table 15
Keywords and associated TF-IDF scores of weather-climate and sea-coast in the DPSIR framework."
Category | Sub-category | Keywords (TF-IDF scores) |
---|---|---|
Driver | Economic driving forces (culture) | Study (0.012), research project (0.007), and analysis (0.005) |
Economic driving forces (security) | Insurance (0.006) | |
Economic driving forces (infrastructure) | System (0.003) | |
State | Environmental state (abiotic state-physical and chemical) | High temperatures (0.012), hailstorm (0.008), and storm (0.007) |
Impact | Ecosystem services (supporting) | Modeling elaborations (0.016) |
Response | Driving forces-based responses | Data (0.052), web site (more user friendly) (0.030), rainfall data (0.020), wind data (0.019), and information (0.016) |
Pressure-based responses | Last data (0.015) and daily data (0.013) | |
Impact-based responses | Annual data (0.013), weather stations (data and localization) (0.013), weather forecast (0.013), temperature (data) (0.012), request (0.011), coordinates (0.008), maps (0.008), intensity (data rainfall or wind) (0.008), improvements in graphics (0.008), find (data) (0.007), receive (data) (0.006), know (data) (0.006), and answer (0.006) |
Table 16
Most salient keywords according to degree and betweenness centrality."
Rank | keywords | Degree centrality | Keywords | Betweenness centrality |
---|---|---|---|---|
1 | Odor* | 69 | Report | 0.242 |
2 | Report* | 59 | Odor | 0.183 |
3 | Presence* | 40 | Municipality | 0.121 |
4 | Municipality* | 39 | Private | 0.089 |
5 | Hours* | 29 | Presence | 0.081 |
6 | Air | 27 | Site** | 0.066 |
7 | Request | 26 | Request | 0.062 |
8 | Noise | 25 | House | 0.054 |
9 | Strong*** | 25 | Noise | 0.050 |
10 | Coming from | 24 | Air | 0.049 |
11 | House | 23 | Hours | 0.048 |
12 | Present | 21 | Coming from | 0.037 |
13 | Factory | 20 | Present | 0.035 |
14 | Very much | 20 | Area | 0.035 |
15 | Area | 20 | Water | 0.033 |
16 | Odors*** | 19 | Factory | 0.032 |
17 | Water | 19 | Data** | 0.031 |
18 | Days | 19 | Days | 0.027 |
19 | Pollution | 17 | Strong smell | 0.025 |
20 | Days | 17 | Strong | 0.021 |
21 | Site | 16 | Complainant | 0.019 |
22 | Data | 16 | Day | 0.019 |
23 | Canal | 15 | Very much | 0.017 |
24 | To smell | 14 | Canal | 0.016 |
25 | Complainant | 14 | Apartment | 0.014 |
26 | Private | 13 | Odors | 0.013 |
27 | Plastic | 13 | Wastes | 0.012 |
28 | Night | 13 | Pollution | 0.012 |
29 | Acoustic | 12 | Slurries | 0.011 |
30 | Acrid | 11 | River | 0.001 |
Table 17
Top 30 interrelationships between environmental complaint keywords in the DPSIR framework."
Category | Interrelationship | Weight |
---|---|---|
Impact-state | Odor-burnt plastic | 65 |
Odor-acrid | 54 | |
Odor-air | 36 | |
Strong smell-burnt plastic | 18 | |
Impact-response | Odor-report | 21 |
Impact-driver | Odor-factory | 18 |
Odors-farms | 13 | |
Impact-pressure | Smelly-fumes | 27 |
Odor-smoke | 13 | |
Odor-spreading manure | 7 | |
Odor-spreading slurries | 7 | |
Pressure-driver | Pollution-installations | 13 |
Pressure-response | Wastewater discharge-report | 10 |
Wastewater discharge-authorization | 7 | |
Pressure-impact | Foam-odor | 8 |
Pollution-noise | 7 | |
Response-impact | Report-noise | 30 |
Report-odors | 14 | |
Municipality-noise | 13 | |
Response-state | Data-air | 7 |
Response-pressure | Report-discharge | 9 |
State-pressure | Water-discharge | 19 |
Stream-foam | 13 | |
Canal-spillage | 10 | |
Canal-foam | 9 | |
State-impact | Water-smelly | 10 |
Canal-odor | 7 | |
Report-trouble | 7 | |
Driver-pressure | Installations-acoustic | 12 |
Factory-fumes | 6 |
[1] | Aguwa C., Olya M.H., Monplaisir L., 2017. Modeling of fuzzy-based voice of customer for business decision analytics. Knowledge-Based Syst. 125, 136-145. |
[2] | Al-Khouri A.M., 2012. Customer relationship management: Proposed framework from a government perspective times. Journal of Management and Strategy. 3(4), 34-54. |
[3] | Arpae (Regional Agency for Prevention, Environment and Energy) Emilia-Romagna, 2022. Fonderie Cooperative di Modena. [2023-06-09]. https://www.arpae.it/it/il-territorio/modena/in-evidenza-a-modena/fonderie-cooperative-di-modena. |
[4] | Batagelj A., Mrvar V., 1998. Pajek-program for large network analysis. Connections. 21(2), 47-57. |
[5] | Bradley P., 2015. Using the DPSIR Framework to Develop a Conceptual Model: Technical Support Document. [2023-06-09]. https://archive.epa.gov/ged/tutorial/web/html/slide0004-3.html. |
[6] |
Brewer B., 2007. Citizen or customer? Complaints handling in the public sector. Int. Rev. Adm. Sci. 73(4), 549-556.
doi: 10.1177/0020852307083457 |
[7] | Bruno M.F., Saponieri A., Molfetta M.G., et al., 2020. The DPSIR approach for coastal risk assessment under climate change at regional scale: The case of Apulian Coast (Italy). J. Mar. Sci. Eng. 8, 531, doi: 10.3390/jmse8070531. |
[8] | Cañas A.J., Novak J.D., Vanhear J., 2012. Concept Mapping and Environment as a Connection. [2023-06-09]. https://cmc.ihmc.us/cmc2012Papers/cmc2012-p26.pdf. |
[9] | Capuano F., de’ Munari E., Forti S., 2018. Il controllo degli odori, nuova frontiera di ricerca. Ecoscienza. 2, 50-51 (in Italian). |
[10] |
Chen Y.C., 2010. Citizen-centric e-government services: Understanding integrated citizen service information systems. Soc. Sci. Comput. Rev. 28(4), 427-442.
doi: 10.1177/0894439309359050 |
[11] | Cook G.S., Fletcher P.J., Kelble C.R., 2014. Towards marine ecosystem based management in South Florida: investigating the connections among ecosystem pressures, states and services in a complex coastal system. Ecol. Indic. 44, 26-39. |
[12] | Cormier R., Kannen A., Elliott M., et al., 2013. Marine and Coastal Ecosystem-Based Risk Management Handbook. Denmark: International Council for the Exploration of the Sea (ICES), 1-60. |
[13] | Deidda Gagliardo E., 2002. La Creazione Del Valore Nell’ente Locale. Torino: Giuffrè Publisher (in Italian), 1-456. |
[14] | Demsar J., Curk T., Erjavec A., et al., 2013. Orange: Data mining toolbox in Python. The Journal of Machine Learning Research. 14(1), 2349-2353. |
[15] | Distante D., Faralli S., Rittinghaus S., et al., 2021. DomainSenticNet: An ontology and a methodology enabling domain-aware sentic computing. Cogn. Comput. 14, 62-77. |
[16] | EEA (European Environmental Agency), 2005a. Sustainable Use and Management of Natural Resources. Copenhagen: EEA, 9-11. |
[17] | EEA, 2005b. EEA Core Set of Indicators—Guide. Luxembourg: EEA, 1-38. |
[18] | EPA (Environmental Protection Agency), 1994. A Conceptual Framework to Support the Development and Use of Environmental Information. Environmental Statistics and Information Division-Making. Washington DC.: United States Environmental Protection Agency, 8-17. |
[19] | European Commission, 1999. Towards Environmental Pressure Indicators for the EU (1st edition). Luxembourg: Office for Official Publications of the European Communities, 1-185. |
[20] | European Commission,Directorate-General for Environment, 2020. Environmental Compliance Assurance Vade Mecum. Luxembourg: Publications Office of the European Union, 1-78. |
[21] | FAO (Food and Agriculture Organization), 2003. Data Sets, Indicators and Methods to Assess Land Degradation in Drylands. Rome: FAO, 99-102. |
[22] | Faralli S., Rittinghaus S., Samsami, et al., 2021. Emotional intensity-based success prediction model for crowdfunded campaigns. Inf. Process. Manag. 58(1), 102394, doi: 10.1016/j.ipm.2020.102394. |
[23] | Gabrielsen P., Bosch P., 2003. Environmental Indicators: Typology and Use in Reporting. Copenhagen: European Environmental Agency, 1-20. |
[24] | Gebremedhin S., Getahun A., Anteneh W., et al., 2018. A Drivers-Pressure-State-Impact-Responses framework to support the sustainability of fish and fisheries in Lake Tana, Ethiopia. Sustainability. 10, 2957, doi: 10.3390/su10082957. |
[25] | Ghodousi M., Alesheikh A.A., Saeidian B., et al., 2019. Evaluating Citizen Satisfaction and Prioritizing Their Needs Based on Citizens’ Complaint Data. Sustainability. 11(17), 4595, doi: 10.3390/su11174595. |
[26] |
Harris Z., 1954. Distributional structure. Word. 10(23), 146-162.
doi: 10.1080/00437956.1954.11659520 |
[27] | Hartmann S., Mainka A., Stock W.G., 2017. Citizen relationship management in local governments:The potential of 311 for public service delivery. In: Paulin, A., Anthopoulos, L., Reddick, C., (eds.). Beyond Bureaucracy. Public Administration and Information Technology. Berlin:Springer, 337-353. |
[28] | ISTAT (Italian national statistical institute), 2023. Resident Population on 1st January: Emilia-Romagna. [2023-06-17]. https://www.istat.it/en/population-and-households? |
[29] | Joung J., Jung K., Ko S., et al., 2019. Customer complaints analysis using text mining and Outcome-Driven Innovation method for market-oriented product development. Sustainability. 11(1), 40, doi: 10.3390/su11010040. |
[30] | Kannabiran G., Xavier M.J., Anantharaaj A., 2004. Enabling e-governance through citizen relationship management: Concept, model and applications. Journal of Services Research. 4, 223-240. |
[31] | Katir O., Sakalli K., Armutlu H., et al., 2020. Determination of customer satisfaction by text mining: Case of Cappadocia Hotels. Journal of Business Reaearch-Turk. 12, 546-556. |
[32] | Khadir A.C., Aliane H., Guessoum A., 2021. Ontology learning: Grand tour and challenges. Comput. Sci. Rev. 39, 100339, doi: 10.1016/j.cosrev.2020.100339. |
[33] | Khunanake W., Pradatsudara A., Pattanakiat S., 2018. Stakeholder involvement in developing environmental indicators for the Lam Nam Yang Part 1 Watershed in the Northeastern Thailand. Applied Environmental Research. 40(3), 28-41. |
[34] |
Larson S., Stone-Jovicich S., 2011. Community perceptions of water quality and current institutional arrangements in the Great Barrier Reef Region of Australia. Water Policy. 13(3), 411-424.
doi: 10.2166/wp.2010.084 |
[35] | Lewison R.L., Rudd M.A., Al-Hayek W., et al., 2016. How the DPSIR framework can be used for structuring problems and facilitating empirical research in coastal systems. Environ. Sci. Policy. 56, 110-119. |
[36] | Liu X., Liu H.T., Chen J.C., et al., 2018. Evaluating the sustainability of marine industrial parks based on the DPSIR framework. J. Clean. Prod. 188, 158-170. |
[37] | Loomis D.K., Paterson S.K., 2014. Human dimensions indicators of coastal ecosystem services: A hierarchical perspective. Ecol. Indic. 44, 63-68. |
[38] | Lucini F.R., Tonetto L.M., Fogliatto F.S., et al., 2020. Text mining approach to explore dimensions of airline customer satisfaction using online customer reviews. J. Air Transp. Manag. 83, doi: 10.1016/j.jairtraman.2019.101760. |
[39] | Malekmohammadi B., Jahanishakib F., 2017. Vulnerability assessment of wetland landscape ecosystem services using driver-pressure-state-impact-response (DPSIR) model. Ecol. Indic. 82, 293-303. |
[40] | Malmir M., Javadi S., Moridi A., et al., 2021. A new combined framework for sustainable development using the DPSIR approach and numerical modeling. Geosci. Front. 12(4), 260-273. |
[41] | Motola V., Colonna N., Alfano V., et al., 2009. Censimento Potenziale Energetico Biomasse, Metodo Indagine, Atlante Biomasse su WEB-GIS. Roma: Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), 1-140. |
[42] | Mozumder M.M.H., Pyhälä A., Wahab M.A., et al., 2019. Understanding social-ecological challenges of a small-scale Hilsa (Tenualosa ilisha) Fishery in Bangladesh. Int. J. Environ. Res. Public Health. 16(23), 4814, doi: 10.3390/ijerph16234814. |
[43] | Neshat A., Pradhan B., Dadras M., 2014. Groundwater vulnerability assessment using an improved DRASTIC method in GIS. Resources, Conservation and Recycling. 86, 74-86. |
[44] | Niemeijer D.S., de Groot R., 2008. A conceptual framework for selecting environmental indicator sets. Ecol. Indic. 8, 14-25. |
[45] | Obubu J., Odong R., Alamerew T., 2022. Application of DPSIR model to identify the drivers and impacts of land use and land cover changes and climate change on land, water, and livelihoods in the L. Kyoga basin: Implications for sustainable management. Environ Syst Res. 11, 11, doi: 10.1186/s40068-022-00254-8. |
[46] | OECD (Organisation for Economic Cooperation and Development), 1993. OECD Core Set of Indicators for Environmental Performance Reviews. A Synthesis Report by the Group on the State of the Environment. Paris: OECD Publishing, 5-11. |
[47] | OECD, 2005. Integrated Environmental Permitting Guidelines for EECCA Countries. Paris: OECD Publishing, 132-138. |
[48] | OECD, 2006. Environment at a Glance:OECD Environmental Indicators. Paris: OECD Publishing, 127-135. |
[49] | Patrício J., Elliott M., Mazik K., et al., 2016. DPSIR-Two decades of trying to develop a unifying framework for marine environmental management? Front. Mar. Sci. 3, 177, doi: 10.3389/fmars.2016.00177. |
[50] | Rapport D., Friend A., 1979. Towards a Comprehensive Framework for Environmental Statistics:A Stress-Response Approach. Ottawa: Minister of Supply and Services Canada, 27-67. |
[51] |
Richter P., Cornford J., 2007. Customer relationship management and citizenship: Technologies and identities in public services. Soc. Policy Soc. 7(2), 211-220.
doi: 10.1017/S1474746407004162 |
[52] | Ponte V., 2015. CiRM:CRM no Setor Público. In: Demo, G., (ed.). Marketing de Relacionamento & Comportamento do Consumidor. Atlas: São Paulo, 137-174 (in Portuguese). |
[53] | Roche C., 2011. Network analysis of Semantic Web Ontologies. California: Standford University, 13. |
[54] | Roy S., Bose A., Majumder S., et al. 2022. Evaluating urban environment quality (UEQ) for Class-I Indian city: an integrated RS-GIS based exploratory spatial analysis. Geocarto International. Doi: 10.1080/10106049.2022.2153932. |
[55] | Roy S., Basak D., Bose A., et al. 2023. Citizens’ perception towards landfill exposure and its associated health effects: A PLS-SEM based modeling approach. Environ. Monit. Assess. 195, 134, doi: 10.1007/s10661-022-10722-4. |
[56] |
Salton G., Buckley C., 1988. Term-weighting approaches in automatic text retrieval. Inf. Process. Manag. 24(5), 513-523.
doi: 10.1016/0306-4573(88)90021-0 |
[57] | Salem M., Bose A., Bashir B., et al., 2021. Urban expansion simulation based on various driving factors using a logistic regression model: Delhi as a case study. Sustainability. 13(19), 10805, doi: 10.3390/su131910805. |
[58] |
Sami A., Irfan A., Qureshi M.I., et al., 2021. Sustainable public value: A step towards green public organisation for a sustainable society. Int. J. Innov. Sustain. Dev. 15(2), 223-233.
doi: 10.1504/IJISD.2021.114330 |
[59] |
Secchi L., 2009. Modelos organizacionais e reformas da administração pública. Revista de Administração Pública. 43(2), 347-369 (in Portuguese).
doi: 10.1590/S0034-76122009000200004 |
[60] | Semeoshenkova V., Newton A., Rojas M., et al., 2017. A combined DPSIR and SAF approach for the adaptive management of beach erosion in Monte Hermoso and Pehuen Co (Argentina). Ocean Coastal Manage. 143, 63-73. |
[61] | Smeets E., Weterings R., 1999. Environmental indicators: Typology and overview. Copenhagen: European Environment Agency (EEA), 19. |
[62] | Sun C.Z., Wu Y.J., Zou W., et al., 2018. A rural water poverty analysis in China using the DPSIR-PLS model. Water Resour. Manag. 32(6), 1933-1951. |
[63] | Sun S.K., Wanga Y.B., Liu J., et al., 2016. Sustainability assessment of regional water resources under the DPSIR framework. J. Hydrol. 532, 140-148. |
[64] | Svarstad H., Petersen L.K., Rothman D., et al., 2008. Discursive biases of the environmental research framework DPSIR. Land Use Pol. 25, 116-125. |
[65] |
Tscherning K., Helming K., Krippner B., et al. 2012. Does research applying the DPSIR framework support decision making? Land Use Pol. 29(1), 102-110.
doi: 10.1016/j.landusepol.2011.05.009 |
[66] |
Twizeyimana J.D., Andersson A., 2019. The public value of E-Government-A literature review. Gov. Inf. Q. 36(2), 167-178.
doi: 10.1016/j.giq.2019.01.001 |
[67] | UNEP (United Nations Environment Programme), 1994. World Environment Outlook: Brainstorming Session. [2023-07-17]. https://www.unep.org/events/Environment Assessment Programe.Nairobi. |
[68] | Zhan J., Loh H.T., Liu Y., 2009. Gather customer concerns from online product reviews-A text summarization approach. Expert Syst. Appl. 36(2), 2107-2115. |
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