Adaptive cycle of tourism system

, 44

Adventure tourism

, 88

Agricultural tourism

, 377–378

Agroforestry

, 36

Agrogastronomy

, 377–378

Agrotourism

, 377–378

Airbnb

, 72, 79–80

Algorithmic bias

, 310

Aloft Hotel in Portland

, 239

Alternative tourism

, 52

Amadeus

, 72

AMP Robotics

, 239

Analytics

, 249

Artificial intelligence (AI)

, 2, 4–5, 73, 86, 102, 106–107, 116, 130–132, 135, 167, 223–224, 247–248, 262, 304–305

effect of AI on Chinese tourism industry

, 121–124

AI-based personalized recommendations for eco-friendly activities and experiences

, 307–308

AI-driven customer experience in green destinations

, 307

AI-driven waste reduction and recycling strategies

, 306–307

AI-powered transportation optimization to reduce carbon emissions

, 307

AI-powered virtual assistants to assist travelers in making sustainable choices

, 308

AI-related case studies and best practices

, 311

applications

, 2, 136–137

applications in sustainable tourism

, 306–308

challenges, opportunities, and future trends

, 177–179

chatbots

, 76–77

and ChatGPT

, 109–110

European hospitality industry

, 106–109

in international tourism industry

, 120–121

service bots and virtual and augmented reality to enhance virtual booking

, 263

smart destinations and AI for rebuilding tourism

, 119–120

and smart destinations in tourism of Hong Kong

, 124–126

smart hospitality

, 105–106

in sustainable tourism development

, 263

sustainable tourism development through

, 264–266

in sustainable tourism in China

, 90–91

tools and technologies

, 170

in tourism

, 102, 105, 248–249

in tourism industry

, 135–136

virtual marketplaces

, 78

virtual tourism with AI-powered tour guides

, 77–78

virtual tours

, 78–79

Artificial intelligence Singapore Groups (AISG)

, 124

Artificial neural networks

, 117–118

Association of Southeast Asian Nations (ASEAN)

, 124

Augmented reality (AR)

, 7–8, 72–73, 105–106, 137–138, 173, 175–177, 240–241, 250, 274, 276

destination marketing and

, 276–279

technologies

, 274

Automated aerial mapping techniques

, 228–229

Automatic translation

, 123

Awareness

, 210

Basic needs approach (BNA)

, 389

Beautiful China’ campaign

, 89–90

Beijing’s Shangri-La Hotel

, 122

Big Data

, 106–107, 228–229, 249

Bio-based resource consumption

, 6

Biometrics

, 106–107

Bitcoin

, 74

Black Sheep Inn, Ecuador

, 151–152

Blockchain

, 73–74, 173

Blogger marketing

, 250

Botlr

, 238–239

Brundtland Commission

, 378

Bruntland Report

, 52–53

Businesses

, 251

Capacity building

, 158

Cape Town Declaration

, 57, 321

Capitals, access and availability of

, 44–45

Car-sharing systems

, 242

Carbon footprint

, 228–229

Chatbots

, 86, 106–107, 137–138

ChatGPT

, 109–110

China

AI in sustainable tourism in China

, 90–91

sustainable tourism in

, 88, 90, 153–154

tourism in

, 87–88

urban regeneration in

, 153

China Green Hotel Alliance

, 121–123

China Green Hotel Standard

, 150

China National Tourism Administration

, 91–92, 121, 123

Chinese government

, 155

polices

, 156

Chinese hospitality industry

, 91–92, 116

Chinese tourism industry, effect of AI on

, 121–124

Circular economy

, 253–254

City-Zen project

, 227–228

Climate change

, 61–62

adaptation

, 62

Climate crises

, 331–332

Cloud computing

, 105–106

Co-occurrence analysis

, 254–257

Collaboration and participation of stakeholders

, 23

Collaborative analysis

, 295–296

Community Based Tourism (CBT)

, 169

Community engagement

, 209–210

Community-led conservation initiatives

, 36

Complementary AI

, 263

Computer vision

, 172

Conscious travel

, 12–13

Conservation

, 44, 304–305

AI and data analytics for

, 308–309

assessing environmental impact and guiding

, 309

and wildlife protection

, 178

Consumer-based destination green brand equity

, 223–224

using AI in destinations

, 228–229

green practices and AI in development of

, 229–230

green practices in destinations

, 226–228

literature review

, 224–226

Contactless technologies

, 250

Content analysis

, 254

Costa Rican Vacations

, 241

COVID-19

crisis

, 11–12, 60

pandemic

, 73, 102, 116, 304–305, 362

and rebuilding tourism

, 118–119

Creative destinations

, 319, 347, 350

Creative tourism (CT)

, 8–9, 237–238, 317–318, 347–348

current trends and applications of robots in

, 240–241

current trends and applications of SSTs in

, 242–243

sustainable development and

, 318–320

Crises

, 360

concept in tourism

, 360–362

management in tourism

, 363–364

pandemic and impacts on tourism

, 362–363

Cross-interpretative analysis

, 190

Crowne Plaza Beijing Sun Palace

, 122–123

Ctrip

, 91

Cultural commodification

, 34–35

Cultural diversity

, 56, 333

Cultural interaction

, 140–141

Cultural preservation

, 36, 158

Cultural restoration

, 150–151

Cultural tourism

, 372

Customer relationship management (CRM)

, 103, 107, 132, 135

Customer service

, 173, 177

Data analytics

, 86, 172

Data privacy and security

, 309–310

Data-driven decision making

, 178

Decarbonization

, 62

Decision support systems (DSSs)

, 254

Decision-making processes

, 168

Deep Blue

, 86–87

Deep learning

, 228–229

Destinations

, 223–224, 304–305, 333–334, 351, 360

using AI in

, 228–229

governance

, 360

green practices in

, 226–228

management

, 6, 286–287

marketing

, 276–279

quality

, 226

Didi Chuxing

, 90–91

Digital assets

, 73–74

Digital technologies

, 279

Digital tourism ecosystem

, 119–120

Digital transformation process

, 2, 102, 250

Digitalization in tourism as emerging front

, 249–250

big data and analytics

, 249

contactless technologies

, 250

mobile applications

, 249

online travel agencies

, 249

social media and blogger marketing

, 250

virtual reality and augmented reality

, 250

Disasters

, 360–362

E-commerce

, 167

Earth Summit

, 52–53

EarthCheck

, 210–211, 321

Eco-pledge

, 227–228

Eco-tourism

, 1–4, 11–12, 88–89, 153–154, 208, 215–216

Eco-tourism destinations (see Green tourism destinations)

Eco-tourism Development Plan

, 155

Eco-tourist

, 208

Ecological restoration projects

, 36

Ecosystem health

, 48

Ecosystem Restoration

, 62, 175

Education

, 210

of stakeholders

, 335–337

Energy

, 334

management

, 95–96

Environment

, 335

Environment, Economy and Equality (3E)

, 378

Environmental awareness

, 6

Environmental conservation

, 209

Environmental degradation

, 158

Environmental ecology

, 350

Environmental impact

, 173

Environmental responsibility

, 55–56

Environmental responsible behaviour

, 351

Environmental restoration

, 150–151

Environmentally responsible tourism

, 88–89

Essential mindset shift/change in perspective

, 21–22

Ethereum

, 74

Ethical Traveller

, 321

Ethnic tourism

, 154

European hospitality industry

, 106–109

European tourism industry

, 102

European Union

, 321

Expedia Group

, 124

Experiential richness

, 56, 333

Exploration

, 44

Facebook

, 102

Facilitative AI

, 263

Finca Luna Nueva Lodge in Costa Rica

, 151

Fintech

, 167

Food waste reduction

, 96

Forbidden City

, 87–88

Fortnite

, 73

Fourth Industrial Revolution

, 130–131

FueTrek

, 239

Gastronomy

, 371–372

tourism

, 371–372, 377–378

Generative AI

, 263

Geographical indication system

, 374–375

Geotourism Program of National Geographic

, 321

Global distribution systems (GDSs)

, 72

Global Family Travels

, 170

Global Sustainable Tourism Council (GSTC)

, 41, 394

Governance

, 334

Great Wall of China

, 87–88

Green brand equity

, 223–224

Green Credit programme

, 155

Green destination management

, 333–335

opportunities and threats in

, 335–337

in Türkiye

, 337–338

Green destinations

, 3, 7, 208, 211, 248–249, 304

AI in tourism

, 248–249

awareness

, 226

digitalization in tourism as emerging front

, 249–250

foundation standards

, 333

image

, 225

literature review

, 250–251

loyalty

, 226

methodology

, 252

monitoring and protecting natural resources and wildlife in

, 308–309

policy recommendations

, 258–259

research methodology

, 252

research questions

, 252

results

, 253–258

sustainable tourism destinations

, 248

tourist’s expectations

, 211–213

tourist’s motivation for visiting

, 213–216

in Türkiye case

, 332–333

Green economy

, 188–189

Green Globe

, 210–211

Green Globe and Sustainable Travel International’s STEP Program

, 321

Green Hotel Certification Program

, 89–90, 153–156

Green ideas for tourism project

, 333

Green places

, 252

Green practices

, 188–189, 223–224, 229–230

in destinations

, 226–228

Green restaurants

, 377

Green tourism

, 3–4, 52, 54, 56, 250–251

definition and characteristics of green tourism destinations

, 55–56

destinations

, 247–248

and post-pandemic era

, 58–62

practices

, 58–59

standards for businesses

, 333

in Türkiye case

, 332–333

Green Vacations

, 241

Greenhouse gas effect (GHG effect)

, 54

Greenwashing

, 34–35

Gross Domestic Product (GDP)

, 91–92

Guests

, 239

Hainan Island Rainforest National Park

, 153–154

Henn na Hotel in Japan

, 86

Hilton Shenzhen Shekou Nanhai

, 122

Hilton Worldwide

, 96

Historypin

, 241–242

Hospitality

, 117–118, 130–131

Hospitality 5.0

, 118–119

Hospitality industry

, 104

in China

, 4–5

Hotels

, 108–109

Human expertise

, 103

Human-centred artificial intelligence

, 86–87

Image recognition

, 123

Inclusiveness

, 62

Indian tourism

, 392–394

Information and communication technologies (ICTs)

, 72, 119–120

Information exchange centre (IEC)

, 117–118

Information processing

, 123

Infrastructure development

, 158

Insight era

, 171

Instagram

, 102

InterContinental Hotels Group (IHG)

, 90–91

InterContinental Shanghai Wonderland

, 122

International Centre for Responsible Tourism (ICRT)

, 57

International Federation of Tour Operators (IFTO)

, 389

International Institute of Communications (IIC)

, 124

Internet

, 279

Internet of Services (IoS)

, 141–143

Internet of Things (IoT)

, 106–107, 118–119, 141, 143, 172, 175, 253–254

Internet-based technologies

, 279

izi.TRAVEL

, 241–242

Job displacement

, 310

Judgemental sampling technique

, 190

Just Ahead

, 241–242

Kerala declaration

, 56–57

Kinki Nippon Tourist

, 239

Language translation

, 177

Large enterprises (LEs)

, 103–104, 132, 135

Leadership

, 23–24, 298

LEED

, 210–211

certification

, 150

Light pollution

, 227

Living system thinking

, 22

Local communities

, 209–210

Local economic vitality

, 56, 332–333

Location attractiveness

, 387

Long Run

, 151–152

Lyft

, 79–80

Machine learning (ML)

, 76–77, 87, 105–106, 132, 135, 172

Market linkages

, 297

McKinsey Global

, 132–135

Medical tourism

, 88

Meta

, 73

Metaverse

, 72–73

COVID-19 impact on

, 73–74

new technologies

, 79–80

opportunities for tourism

, 76–79

on tourists’ behaviour and experience

, 75–76

virtual world of possibilities

, 74–75

MGM Resorts International

, 96

Ministry of Culture and Tourism

, 155

Mobile applications

, 249

Mobile commerce

, 72–73

Mobility-as-a-Service (MaaS)

, 96

Monitoring and evaluation of regenerative tourism

, 296

Naked Castle in Moganshan

, 150–151

National Ecological Civilization Pilot Zone

, 155

National Sustainable Tourism Standards

, 153–154, 156

National Tourism Administration

, 150

Natural landscape tourist resources (see Natural tourism—resources)

Natural Language Processing (NLP)

, 76–77, 132, 135, 172, 177

Natural Resource Conservation

, 174

Natural tourism

, 88

resources

, 130–132

Nature-based solutions

, 168

Nature-based tourism

, 248

Near-field communication (NFC)

, 141–143

New technologies

, 79–80

Noise

, 227

Nonfungible tokens (NFTs)

, 73–74

Online travel agencies

, 249

Optimisation algorithms

, 172–173

Organic farming

, 375–377

Organic products

, 375–377

Organizational learning

, 360

Our Common Future

, 52–53

Over-reliance on technology

, 310

Over-tourism

, 34

Pakistan

, 392–394

Panarchy

, 44

Pandemic and impacts on tourism

, 362–363

Perceived quality of green destinations

, 226

Personal assistants

, 87

Personalised experiences

, 176

Personalised Sustainability Recommendations

, 174

Personalised sustainable experiences

, 178

Personalized recommendations

, 96

Physical infrastructure

, 335

Place-based model

, 22–23

Policy and planning

, 289–292

Policy and regulation

, 158

Post-pandemic, RT and

, 13–14

Post–COVID-19

, 298

Predictive analytics

, 177

Principle-based approach

, 290–291

Property management systems (PMS)

, 103, 108–109, 132, 135

Qualitative data analysis technique

, 252

Rebuilding tourism

COVID-19 and

, 118–119

smart destinations and AI for

, 119–120

Recommender systems

, 172

Regeneration

, 12–13

Regenerative bioeconomy

, 188

Regenerative Development Framework

, 43

Regenerative leaders (RLs)

, 23–24

Regenerative tourism (RT)

, 1–3, 8, 11–12, 35–36, 149–151, 153, 156–157, 168, 188–189, 286, 304, 317–318, 379, 381

achieving enduring positive impacts

, 19

AI and regenerative tourism in China

, 158–159

background of regeneration and tourism

, 12–13

challenges

, 46, 48, 157–158

characteristics

, 17–18

core concepts and objectives

, 40–41

core values, principles and objectives of

, 39–40

defining concept

, 14–15

effects

, 159–160

governmental support for

, 155

heal, restore and flourish

, 19–20

indicators

, 45–46

linkages between actors

, 296–297

model

, 43–46

objectives

, 19–20

opportunities

, 46–48

paradigm and principles

, 20–24

philosophy

, 293

policy and planning

, 289–292

and post-pandemic

, 13–14

principles

, 41, 43, 289

sustainability to

, 16–19

sustainable destination

, 288–289

sustainable development and

, 323–326

well-being

, 20

Regenerative Tourism Movement

, 5, 168

AI in

, 170–175

AI-driven solutions

, 175–177

benefits of AI in

, 173–175

global initiatives and organisations

, 169–170

impacts

, 168–170

Regenerative travel

, 166

Release

, 44

Renewable energy

, 36

Renewing

, 34

Reorganization

, 44

Research design

, 391

Resilience

, 296

Responsible destination

, 350–354

Responsible tourism (RT)(see also Sustainable tourism)

, 8–9, 39, 51–52, 56, 58, 237–238, 247–248, 317–318, 344

current trends and applications of robots in

, 238–240

current trends and applications of SSTs in

, 241–242

definition and principles

, 57–58

and post-pandemic era

, 58–62

practices

, 58–59

prominent concepts in

, 322

sustainable development and

, 320–323

Responsible tourists

, 352

Responsible Travel

, 241

Restoring

, 34

ReT (see Regenerative tourism (RT))

Revenue management systems (RMS)

, 103, 132, 135

Revitalization

, 36

Rio Declaration

, 52–53

Roblox

, 73

Robotics

, 130–131, 237

applications

, 103

intelligence

, 87

Robotized intelligence

, 87

Robots, AI, and Service Automation (RAISA)

, 106–107

Robots

, 2, 176, 238–240

Sabre

, 72

Scooter-sharing

, 242

Self-service bike-sharing

, 242

Self-Service technologies (SSTs)

, 104, 132, 135, 237

current trends and applications

, 241–243

Sensors

, 229

Service delivery

, 103

Shanghai Jinjiang Hotel

, 122

Shinkansen system

, 140–141

Sichuan Giant Panda Sanctuaries

, 153–154

Singapore’s AI-Powered Smart Nation Initiative

, 311

Single Stream Recyclers facility

, 239–240

Small and medium-sized enterprises (SMEs)

, 155

Smart city’ concept

, 117, 121, 167

Smart destinations

, 4–5, 116–118, 279–280

AI and smart destinations in tourism of Hong Kong

, 124–126

and AI for rebuilding tourism

, 119–120

AI in international tourism industry

, 120–121

effect of AI on Chinese tourism industry

, 121–124

COVID-19 and rebuilding tourism

, 118–119

management

, 177–178

Smart ecosystem

, 117–118

Smart Energy Management

, 174, 311

for hotels and accommodation

, 306

Smart healthcare

, 167

Smart hospitality

, 105–106

Smart hotels

, 176

Smart manufacturing

, 167

Smart technologies

, 6

Smart tourism

, 117, 167, 189

destinations

, 121

Smart transportation

, 96, 177

Smartphones

, 72–73

Social media

, 102, 250

solutions

, 360

Social structure and culture

, 334

Social sustainability

, 153

Social upheaval

, 34

Speech recognition

, 123

Stakeholders

, 333–335

education

, 335–337

engagement and communication

, 178

in Türkiye

, 337–338

Sun lust

, 214

Sustainability

, 9, 37–38, 52, 54, 116, 149–150, 186, 304, 371–373, 386

awareness and education

, 308

Chinese hospitality industry in

, 92–94

to regenerative tourism

, 16–19

Sustainability Tour

, 169

Sustainable destination

, 286–287

development

, 294–295

formulation of policy

, 292–298

paradigm shift

, 287–288

regenerative tourism

, 288–289

Sustainable development

, 52–53, 89, 263, 314, 386

and creative tourism

, 318–320

and regenerative tourism

, 323–326

and responsible tourism

, 320–323

Sustainable economy

, 335

Sustainable expansion of tourist industry

, 86

AI in sustainable tourism in China

, 90–91

challenges

, 94

Chinese hospitality industry

, 91–92, 94

steps to tackle challenges

, 95–96

sustainable tourism in China

, 88–90

tourism in China

, 87–88

Sustainable farming

, 36

Sustainable future in tourism

, 267–268

Sustainable gastronomy

agrotourism and agrogastronomy

, 377–378

geographical indication system

, 374–375

green restaurants

, 377

movements

, 374–378

organic products and organic farming

, 375–377

and tourism

, 373–374

Sustainable infrastructure

, 36

Sustainable mobility solutions

, 178

Sustainable regeneration

, 38

Sustainable resource management using AI algorithms

, 307

Sustainable Restaurant Association

, 377

Sustainable solutions

, 17

Sustainable tourism

, 1–2, 4, 34, 37, 39, 51–53, 118–119, 149–150, 156–157, 166, 186–187, 247–248, 304–305, 320, 350–351, 371–372, 386, 388

access to data

, 310

through AI

, 264–266

AI and data analytics for conservation efforts

, 308–309

AI applications in

, 306–308

AI-powered tools and technologies

, 172–173

AI-related case studies and best practices

, 311

benefits of AI in

, 173–175

challenges associated with

, 390–391

challenges in implementing AI solutions

, 310–311

characteristics

, 17–18

in China

, 88–90

and China

, 153–154

cost and expertise

, 311

cultural relevance

, 311

destinations

, 248

development

, 262–264

emergence of artificial intelligence in tourism

, 305–314

ethical concerns

, 309–310

grey area for research

, 389–390

India and Pakistan

, 392–394

infrastructure and connectivity

, 310

literature review

, 263–264, 390–391

positive and negative impact of using AI

, 266–267

positive impact

, 312–314

practices

, 209–210

research methodology

, 391

sustainable future in tourism

, 267–268

theoretical framework and tourism

, 388–389

in Türkiye case

, 332–333

Sustainable tourism and gastronomy

, 378–379

Sustainable Tourism Conference

, 321

Sustainable tourist destinations (see Green tourism destinations)

Sustainable Transportation

, 174

Technology-driven practices

, 6

Terracotta Warriors

, 87–88

Third World Ski resort

, 214

Tourism

, 1–2, 12–13, 52, 54, 72, 304, 360, 386

AI and role in

, 102, 105, 248–249

in China

, 87–88

crisis concept in

, 360–362

crisis management in

, 363–364

development

, 35, 262

emergence of artificial intelligence in

, 305–314

pandemic and impacts on

, 362–363

policy

, 130–131

SDGs for

, 187–188

superstructure

, 297–298

Tourism destination

, 386

Tourism industry

, 2, 166

artificial intelligence applications

, 136–137

artificial intelligence in

, 135–136, 141, 143

benefits of artificial intelligence applications

, 137–138

challenges of artificial intelligence applications

, 138–139

in Europe

, 4–5

of Japan

, 139–141

Tourism living system (TLS)

, 20–21

Tourists

, 208, 227, 360

destinations

, 253–254

expectations

, 211, 213, 346–347

gaze

, 140–141

green destinations

, 208–211

motivation for visiting green destinations

, 213–216

motivations

, 344–346

Transformational bioeconomy

, 188–189

Transformational technologies

methodology

, 189–190

outcome and implications

, 190–191

qualitative data analysis tabular representation

, 192

regenerative tourism

, 188–189

scope for research

, 191–200

SDGs for tourism

, 187–188

smart tourism

, 189

sustainable tourism

, 186–187

Transportation

, 307

Travel

, 72

Travel and Audio (T&A)

, 241–242

Travel career pattern (TCP)

, 214–215

Travellers

, 6

Travelstride

, 241

Triple bottom line

, 38

Turing machine

, 130–131

Turing test system

, 130–131

Türkiye

, 332

education of stakeholders and opportunities and threats in green destination management

, 335–337

green destination management and stakeholders

, 333–335

green destination management and stakeholders in

, 337–338

recommendations

, 338–339

sustainable tourism, green tourism and green destinations

, 332–333

Uber

, 72, 79–80

United Arab Emirates (UAE)

, 132–135

United Nations Development Program (UNDP)

, 337–338

United Nations Environment Programme

, 250–251

United Nations’ Sustainable Development Goals (SDGs)

, 88–89, 186, 251, 317–318

for tourism

, 187–188

United Nations’ World Tourism Organisation (UNWTO)

, 52–53, 120, 304–305, 321, 350, 352–353, 361–362

Urban green tourism destination

, 55

Urban regeneration in China

, 153

Urban tourism

, 88

Vehicle2grid project

, 227–228

Virtual assistants

, 86

Virtual hotels

, 75

Virtual marketplaces

, 75–76, 78

Virtual reality (VR) (see also Augmented reality (AR))

, 72–73, 105–106, 137–139, 173, 175–177, 240–241, 250, 274, 276

Virtual space

, 76–77

Virtual tourism with AI-powered tour guides

, 77–78

Virtual tours

, 75, 78–79

guides

, 4

Virtuality continuum

, 275–276

Vision

, 294

VoiceMap

, 241–242

Volatility, uncertainty, complexity, ambiguity (VUCA)

, 296

Wanderlust

, 214

Waste reduction and recycling

, 311

Water and Waste Management

, 174

Water conservation

, 96

Well-being

, 20

World Commission on Environment and Development (WCED)

, 52–53

World Summit on Sustainable Development (WSSD)

, 321

World Tourism Organization (WTO)

, 250–251, 332, 344, 371–372, 387

World Travel and Tourism Council (WTTC)

, 371–372